Sex- and age-specific foraging tactics in grey seals (Halichoerus

grypus) revealed by behaviour discriminating state-space

models.

Greg A. Breed1,2, W. Don Bowen3, Marty L. Leonard2, Ian D. Jonsen3, and RansomA. Myers4

ICES Halifax, Session PSeptember 23, 2008

1Corresponding author: Greg A. Breed, Email: gbreed@dal.ca2Department of Biology, Dalhousie University, 1355 Oxford Street, Halifax, NovaScotia B3H 4J1, Canada.3Bedford Institute of Oceanography, 1 Challenger Drive, Dartmouth, Nova Scotia,B2Y 4A2, Canada.4Posthumus

Abstract

Identifying where and how animals forage is key to understanding their ecology andecosystem role. In many large pelagic animals, observing behaviour is limited almostexclusively to telemetry studies. Discriminating different behaviours from trackingdata has been a key, but often elusive, goal. Here we use state-space models (SSMs)to fit a correlated random walk model that switches between two unobservedbehavioural states (nominally foraging and travelling) to 84 (41 male and 43 female)adult, 24 young-of-year (12 male, 12 female), and 6 2-3 yr old juvenile grey seal(Halichoerus grypus) satellite telemetry tracks. We use the method’s ability toestimate locations from error prone satellite observations and classify behaviouralstate to quantify differences in foraging strategies among four demographic groups.SSM results reveal markedly different spatial behaviour between the male and femaleadults, fitting well with sexual size dimorphism and known dietary differences andsuggesting males and females deal with seasonal prey availability and reproductivecosts differently. We found similar seasonal foraging patterns in prereproductive agedanimals, but those groups did not show sex related differences and had simplerseasonal foraging patterns that were not influenced by reproductive timing. SSMresults were also able to produce behaviourally informed habitat use maps, showing acomplex and dynamic network of small, intensely used foraging areas on the ScotianShelf. Our flexible SSM approach clearly demonstrates sex and age relatedbehavioural differences, fine scale spatial and temporal foraging patterns, and aclearer picture of grey seal ecology in the Scotian Shelf ecosystem.

Keywords: animal movement, foraging, correlated random walk, switching model,seal

Introduction

Foraging is central to an animal’s life history and ecology. Appropriately synchronizing

foraging effort with reproductive costs, seasonal cycles, and environmental variability

can mean the difference between success or failure of individuals or whole populations.

For most pelagic marine animals, behaviours at sea, including foraging, are nearly

impossible to observe directly. Instead, biologists have been attaching increasingly

sophisticated electronic tags that record or transmit location, physiological, and envi-

ronmental parameters. Satellite telemetry and other forms of tracking have filled vast

gaps in our knowledge of ecology and natural history of many marine species (eg. Stew-

art et al. 1989; Jouventin and Weimerskirch 1990; McConnell et al. 1992; Prince et al.

1992; LeBoeuf et al. 2000; Shaffer et al. 2006). The extent of ranging by species such

as northern elephant seals (Mirounga angustirostris), wandering albatross (Diomedea

exulans), or sooty shearwaters (Puffinus griseus) was far beyond expectation. Tag-

ging studies today are growing in number as tags become smaller and more reliable.

Thousands of animal tracks have been logged around the world.

State-space models (SSMs) have recently emerged as a powerful way to analyze

tracking data (Anderson-Spreher and Ledolter 1991; Newman 1998; Sibert et al. 2003;

Jonsen et al. 2003, 2005, 2007; Breed et al. 2008). The approach differs from other meth-

ods because it simultaneously fits 2 kinds of error: measurement error (how well the

location is known) and process noise (how much an animal’s movement deviates from

the model being fit). SSM implementations have been around for many years (Kalman

1960), but only recently have numerical methods such as Markov Chain Monte Carlo

(MCMC) simulations or particle filters become available to solve non-linear and/or

non-Guassian SSMs (Gelfand and Smith 1990; Doucet et al. 2001). These advances

have allowed SSMs to be adapted for use as tools for animal movement analysis.

We used a state-space approach to analyze the movements of grey seals (Hali-

choerus grypus) in the North-West Atlantic. Grey seals are sexually size-dimorphic,

and use an intense capital breeding strategy whereby both sexes use energy stored

months before reproduction to fuel the cost of mate acquisition (males) or lactation

1

(females). Differences in body size and breeding strategy between males and females

are reflected in significant differences in diet, diving behaviour, and blubber accumula-

tion (Iverson et al. 1993; Beck et al. 2003b,c, 2005, 2007; Lidgard et al. 2005). Although

these differences have been clearly demonstrated, it is not clear if they are life history

adaptations of each sex, niche separation due to size difference, or some combination

of these factors. Diet differences are strongly seasonal (Beck et al. 2007), and sexual

segregation also has a strong seasonal component (Breed et al. 2006). Differences are

strongest in mid-winter just after the January breeding season when males range much

farther south and expand their diets to include a broader range of prey than females.

Diet and ranging differences between adult males and females should be reflected

in behaviour, foraging trips, and habitat preference. To test for behavioural differences,

we used the (Jonsen et al. 2005) behaviour discriminating SSM to infer behaviour at

each location. We then broke tracks into individual foraging trips, using the inferred

behavioural state to see how trip structure differed between sexes and through the

year. In addition, we similarly analyzed tracks from two younger prereproductive age

classes to to compare with adults. We tested the hypothesis that foraging is seasonal

in all age classes, and not just the reproductive adults. Testing this hypothesis will

indicate if seasonal changes in foraging behaviour are due entirely to reproductive cycles

or if seasonal environmental conditions such as prey distribution significantly impact

foraging behaviour.

Methods

Data Collection. From 1995 to 2004, 81 Argos satellite telemetry tags were deployed

on adult grey seals hauled out at Sable Island, Nova Scotia (44.0◦N 60.0◦W; Fig. 1),

the largest grey seal breeding colony in the world (Bowen et al. 2007). In late May

2004, twenty-four (12 male, 12 female) young-of-year (YOY) and 6 2-3 yr old juveniles

were similarly tagged. Tags were deployed according to methods describe in Bowen

et al. (1999) and Austin et al. (2003).

2

Fig. 1. Study area showing Sable Island and prominent banks. Contour line is the 100m isobath.

State-Space Model. In total, the dataset available for analysis includes over 120

telemetry tracks. These tracks were fit to the behavioural state switching SSM model

described in Jonsen et al. (2005) and Breed et al. (2008). Long duty cycles combined

with poor tag quality in early deployments occasional prevented models from fitting

properly. In total we were able to fit the switching model to 111 tracks, including 81

adults, 24 young of year, and 6 2-3 yr old juveniles.

The core of the SSM model is the transition equation, in this case a correlated

random walk (CRW) described by:

dt ∼ γTdt−1 +N2(0,Σ) (1)

where dt−1 is the distance between locations xt−1 and xt−2, and dt is the distance

between the locations xt and xt−1. dt and dt−1 are 2 element vectors representing

differences in latitude and in longitude between consecutive positions on the track

x. Since time steps are regular, these are directly related to an animal’s speed and

thus behaviour. Motion is autocorrelated in both direction and speed, where γ is the

3

correlation coefficient ranging from 0 to 1. T is a transition matrix that relates the turn

angle, which is polar, to the Cartesian coordinates of the data and location estimates

(see Jonsen et al. 2005; Breed et al. 2008).

The model switches between 2 states, classifying behaviour at each location into

2 categories (nominally foraging or travelling), based upon the movement characteris-

tics at each location. To do this, the CRW model tracks two sets of parameters that

describe movement. One state has high turn angles (averaging 180◦) and low autocor-

relation (< 0.5) representing the “foraging” state. The opposing state has small turn

angles (averaging 0◦) and high autocorrelation (near 1) and represents the “travelling”

state. A given location will be categorized according to the set of parameters that fits

better at that location.

SSMs track observation error using a separate observation equation which relates

the probability of the true location given the error distribution of observation method.

Where the probability distributions of both the transition and observation equations

are maximized is the state-space predicted state of the system. A complex integral

was required to fit the SSM; this integral was estimated using a Markov Chain Monte

Carlo (MCMC) simulation method with the software package WinBUGS.

After fitting tracks, a we used the state estimates in a number of secondary

analysis to understand biology. First, to visualize differences in movement between

model inferred behaviours, we devised a simple graphical method for depicting persis-

tence of speed and direction between consecutive moves. This method adds the first

differences (∆x, ∆y, the elements of dt) of two consecutive moves. We call these vec-

tors additive first difference (AFD) vectors. The origin of each AFD vector is [∆xt,

∆yt], it represents the displacement of the first move. The terminus is [∆xt + ∆xt+1,

∆yt + ∆yt+1]; indicating if the next move added to or canceled out the previous move.

Thus AFD vectors of large magnitude pointing away from the origin indicate more

persistent motion, with animals moving quickly in a consistent direction.

Trip Analysis. Many marine mammals and birds organize foraging into trips at sea

punctuated by periods ashore. In grey seals, foraging trips last between 1 and 30 days

4

and most have 3 clear segments. The first is an outbound period of rapid and direct

travel between haulout and foraging areas. This is usually followed by a period spent

foraging. Finally, a period of directed travel returns the animal to a haulout site. Not

all trips fit this pattern; some include travel segments between multiple foraging areas,

some include no apparent foraging, and some no apparent travel.

SSM fits produced location estimates at regular 8 hour intervals and an esti-

mate of behavioural state at each location. The results can be used to objectively

discriminate trip segments using changes in inferred behavioural state. The time spent

in each segment, number of foraging areas visited, trip length, distance to foraging

areas, travel time and tortuosity, and a number of other trip properties can be used to

compare trip structure through the year and among age classes.

Trips were defined conservatively. They began as soon as an animal moved more

than 2 km from any shoreline, and ended when they returned to within 2 km of land.

In addition, animals must spend at least one day (3 consecutive locations) beyond 2 km

for the trip to be included in analysis. Trips were defined this way because the spatial

and temporal resolution of the data make it difficult to resolve shorter trips, even after

fitting with SSMs. Finally, trips that began at Sable Island and ended elsewhere (or

vice-versa) were not included in the analysis because they were rare and because part

or all of the trip was migratory in nature.

Once trips were identified, trip segments were determined using the behavioural

categorization of the SSM model (Fig. 2). During trips, whenever behaviour switched

to and remained in the foraging state for at least 3 consecutive locations (1 day), we

considered those foraging locations a discrete foraging patch, and the length of time

spent in the foraging state to be patch residence time. Most trips had just one foraging

patch, and the subsequent travel segment usually returned the animal to haulout, but

if not, additional foraging and travel segments were discriminated the same way. Since

the SSM does not always classify behaviour with certainty and uncertain locations

almost always appear at behavioural switches, uncertain locations were included in

travel segments and not foraging patch segments.

5

Fig. 2. Trips were divided into segments using SSM behavioural state estimates. Greypoints are travel, black foraging, and open dots uncertain. Most trips contained a clearforaging patch, and included outbound and inbound travel segments on either side ofthat patch.

For the trip analysis, we examined 7 trip characteristics. Trip duration, patch

residence, patch distance, foraging ratio, travel time, travel speed, and travel tortuosity.

Because locations in very short trips or very near-shore trips were excluded from the

trip analysis but were still valid locations, we also analyzed two basic properties of all

SSM estimated foraging locations - water depth at those locations and their distance

from the nearest shoreline.

Trip duration and patch residence are straight-forward measures of trip prop-

erties. The other five measures were derivative. Patch distance is the distance from

the centroid of the patch to the haulout location from which the trip began. Foraging

ratio the fraction of locations categorized as foraging divided by the total number of

locations in that trip. Recalling that location estimates are evenly spaced, this relates

directly to the fraction of time spent foraging. Travel time was the total number of

6

locations in the outbound travel segment which is easily translated into time. Travel

speed and travel tortuosity were calculated only for the first travel segment, and only

for trips with outbound travel segments of at least 1 day. Travel speed was the sum

of distances between locations in the outbound travel segment divided by travel time.

Finally, travel tortuosity was calculated from the sum of distances between locations

in the outbound travel segment divided by the patch distance. Travel tortuosity of 1

means travel distance was exactly the same as patch distance and therefore travel was

perfectly direct; increasing values indicate less direct travel to foraging patches.

Following trip analysis, we used an number of statistical and visualization ap-

proaches to understand difference in trip structure and the distribution of foraging

points. These include kernel density overlap methods and linear mixed-effects models.

These methods are described elsewhere (Breed et al. 2006, 2008).

Results

Model performance. The model detected 2 behavioural states in all tracks. A

number of informal tests and inspections of posterior distributions suggest MCMC

runs converged and model fits were consistent across the data set. Results from a

representative track are shown in are shown in Fig. 3.

Plots of additive first difference (AFD) vectors suggest that SSM inferred forag-

ing and travelling moves differed greatly. Foraging vectors are tightly clustered around

the center and most cross zero in at least one dimension (Fig. 4). Nearly all travel

vectors point outward, and did not cross zero. The two categories occupy different

space on these plots, suggesting marked differences in the spatial properties of each of

the two categories. Foraging moves are short, often reversals of previous moves, while

travel moves almost always add to the previous move to continue in the same direction.

Travel displacements were much larger than foraging displacements.

7

Fig. 3. SSM fit of a representative track. Foraging locations are blue and travel arered; grey-white line indicates the raw Argos track. Yellow points indicate locations ofuncertain behavioural classification (with MCMC travelling:foraging proportions from0.3 to 0.7). Since grey seals are shelf animals, only depths shallower than 500 meters(black) are contoured. Black line is the 100 m contour; Sable Island (44.0◦N 60.0◦W)is highlighted in bright green.

Distribution of foraging locations.

In all demographic groups, foraging areas showed clear seasonal dynamics. In the

summer, foraging typically occurred nearer Sable Island and other shorelines. In fall,

Middle Bank, north of Sable, was heavily used, especially by females (Fig. 5). Foraging

locations in the winter were much more scattered in all groups, and always occurred

farther from shorelines and in deeper shelf waters than during the rest of the year. In

all seasons a striking correspondence between foraging locations and underwater banks

is present.

Similar seasonal foraging patterns also occurred in adult males, YOY, and juve-

niles. Although the trend of seasonal dynamics was similar, each of these demographic

8

Fig. 4. AFD (Additive First Difference) vectors for moves between consecutive inferredforaging locations (blue) and between consecutive inferred travel locations (red) for seal6124. AFD vectors are defined as [∆xt, ∆yt] at the origin of the vector and [∆xt+∆xt+1,∆yt + ∆yt+1] at the terminus. AFD vectors where inferred behaviour differs betweenconsecutive locations are not shown.

9

Fig. 5. Inferred foraging locations for all adult females grouped by season (blue pointsa-c). 18 individuals are represented in (a), 12 in (b), and 34 in (c). In (d) all modelledlocations for the 43 tracked females are plotted for the entire year, with inferred travelshown as red points and uncertain locations in yellow. The 100 m isobath is drawnand Sable Island (44.0◦N 60.0◦W) is shown in bright green. The Gully, a submarinecanyon between Sable, has very few inferred foraging locations, while Middle bank justto the north and framed by the square is a primary foraging area for adult females.

10

Table 1. Kernel density overlap randomization results. 3 separate tests are shown.The first, male vs female YOY, indicates overlap between male and female YOY couldbe generated randomly and thus use similar spatial areas. Overlap between YOY andadult females, was significantly smaller than expected by chance during most months.Adult males and YOY overlapped to a greater degree than adult females, but duringsome months overlap was still smaller than would be produced by chance. Overlapswere divided by the area of the larger home range (bounded by the the 98.5% contour)to create a normalized percentage.

YOY sexual segregation YOY vs Adult Females YOY vs Adult MalesMon Ovrlap Random p Ovrlap Random p Ovrlap Random pJun 34.5 33.6± 0.3 0.21 33.1 34.5± 0.4 0.05 26.4 32.3± 0.4 0.07Jul 38.4 41.5± 0.3 0.24 17.6 34.1± 0.3 <0.002 33.3 36.5± 0.3 0.82Aug 36.0 35.7± 0.4 0.06 16.0 35.7± 0.3 0.04 23.0 34.7± 0.3 0.64Sep 24.8 30.5± 0.4 0.08 26.9 32.4± 0.3 0.10 21.6 34.8± 0.4 0.13Oct 40.3 35.0± 0.4 0.38 32.7 44.6± 0.4 0.002 26.7 38.5± 0.3 0.40Nov 31.4 31.5± 0.4 0.26 34.1 47.9± 0.4 0.04 15.4 37.4± 0.3 0.006Dec 36.8 36.2± 0.4 0.44 32.1 33.5± 0.3 0.18 30.0 36.1± 0.4 0.04Jan 11.9 17.8± 0.3 0.17 5.1 31.8± 0.4 <0.002 9.7 33.0± 0.3 <0.002

groups utilized different areas of the shelf. Areas used by males, females, and YOY

overlapped significantly less than would be expected by chance for large periods of the

year (Table 1 for adults vs YOY, and see Breed et al. 2006, for a comparison of adult

males to adult females), meaning that the foraging areas among these groups differ

significantly.

To visualize these difference in habitat use, we produced simple kernel density

estimate using SSM foraging locations from males, females, and YOY, and then over-

layed them by subtracting one kernel from the other. Where habitat use is the same,

this produces a value of 0, and differences between groups are highlighted by positive

and negative kernel density anomaly.

During the summer and fall, kernel density anomalies show that areas off the

east and west tips of Sable Island were used more heavily by YOY than adults, while

areas north and south of the island’s center were used more heavily by adults than

YOY (Fig. 6). YOY also used Banquereau and western Sable Bank more frequently

than adults (Figs. 7 & 8). In general, Middle Bank, northwest of Sable Island, was

used more heavily by adults, particularly adult females. During winter, YOY moved

away from Sable and did not use regions near Sable Island as frequently as adults,

11

Fig. 6. Kernel density anomaly of foraging locations for adult females vs YOY fromJul to Sep in areas nearest Sable Island. White points are YOY while black are adults.Blues indicate areas used more heavily by YOY while yellows and reds indicate regionsused more heavily by adults. Green areas were used equally.

and instead foraged over banks scattered across the northern half of the Scotian Shelf.

It should be noted that during winter adult males migrate south, away from Sable

Island, and only a fraction of adult male data are represented in the winter anomaly

plot (Breed et al. 2006).

Trip Organization & Habitat Preference of Adults, Juveniles, and YOY.

Habitat Preference: Distance from shore. Distance from shore of foraging lo-

cations differ among YOY, juveniles, and adults. On average, YOY foraging locations

are farther from shore than juveniles or adult females, but were similar to adult males.

YOY and juveniles had similar, nearly identical seasonal patterns, but YOY foraging

12

Fig. 7. Kernel density anomaly of foraging locations for adult males vs YOY fromJul–Sep (a), Oct–Dec (b), and Jan–Apr (c). See Fig. 6 legend for explanation ofcolour.

13

Fig. 8. Kernel density anomaly of foraging locations for adult females vs YOY fromJul–Sep (a), Oct–Dec (b), and Jan–Apr (c). See Fig. 6 legend for explanation of colour.

14

locations were nearly 20 km farther from shore than juveniles during all months except

Jun (Table 2; Fig. 9b). The seasonal pattern included 2 peaks; a large peak in mid-

winter and smaller peak in Jun. From early summer to late fall YOY and especially

juvenile foraging locations moved closer to shore, though a small peak is apparent in

this trend around year day 270. The other trough between these peaks occurred in

Apr-May during a period of sparse data. It is possible the winter and Jun peaks are

connected, but the model is fitting poorly due to sparse data.

Adults showed a more complex seasonal pattern than YOY or juveniles. Peaks

in distance from shore occurred just before and just after the Dec-Jan breeding season,

as well as in Jun following the moult (Fig 9a). Adult males showed a stronger seasonal

pattern with higher peaks than adult females, YOY or juveniles. Both sexes, but

particularly females, foraged closer to shore during summer.

Habitat Preference: Water Depth. A seasonal pattern similar to distance from

shore was present in water depth at foraging locations. Water depth at foraging loca-

tions increases in winter and decreases in summer, though the effect is less dramatic

than changes in distance from shore. The seasonal pattern of water depth preference of

YOY is again very similar to the juvenile pattern, with peaks in Jun, early fall around

year day 270, and through the winter (Fig. 9d). Though the seasonal patterns were

the same, YOY consistently foraged in shallower water than juveniles in the summer

and fall. In winter they foraged in waters of similar depth.

During summer and fall, YOY and juveniles foraged in similar water depths as

adults. Like YOY and juveniles, adults increasingly foraged in deeper water in late fall,

though males showed a dramatic shallowing of foraging locations around the breeding

season followed by an equally dramatic increase in water depth in midwinter after

the breeding season. Neither YOY nor juveniles showed the midwinter shallowing of

adult males, nor did they increase the depth of water used to the degree of adults in

midwinter (Fig. 9c).

Trip structure: Foraging Effort. The seasonal patterns evident in habitat pref-

erences are reflected in many trip properties. However, mixed-effects model fits to

15

Fig. 9. Mixed effects model fits for foraging location distance from shore (a-b) andwater depth (c-d) for adult males (blue), adult females (red), YOY (green), and 2-3y old juveniles (black). Dashed lines are the 95% CI of the mean. Note the strikingsimilarity of of YOY and juvenile seasonal patterns, and that unlike adults, they donot show an increase in distance from shore or water depth just before and after thelate December through January breeding season. Instead, YOY and juveniles show asingle winter peak in these habitat preferences. All demographic groups show peaksin these measures in June, just after the spring moult. Finally, note that during muchof the year, YOY are foraging farther from shore than adult females and 2-3 y oldjuveniles, and in December and January are farther from shore than all other groups.

16

trip properties have larger confidence intervals and differences are harder to detect.

This is because a trip accounts for a single observation, but it may contain dozens of

foraging locations, reducing the effective sample size relative to the habitat models.

Nonetheless, significant seasonal and demographic differences are present in many trip

attributes. Five of the attributes we measured can be considered proxies for foraging

effort: trip length, patch distance, travel time, patch residence and foraging ratio.

YOY, juveniles, and adults structured trips differently. The average trip length

for YOY was 8.6 days, about 3 days longer than juvenile trips, 2 days longer than adult

female trips, and 1 day longer than adult male trips (Table 2). These were yearlong

averages, but superimposed on different seasonal cycles, groups were more similar or

different depending upon the time of year.

Not surprisingly, patch distance and travel time followed similar seasonal pat-

terns to foraging location distance from shore, with patch distance related to distance

from shore and travel time related to patch distance. YOY patches were, on average,

farther away than adult patches. Juveniles showed the same seasonal pattern as YOY,

but patches were closer than YOY or adults. Adult males showed very large increases

in patch distance just before and after the breeding season. YOY and juveniles had a

single large peak in patch distance during winter and a trough in the summer, while

adults showed two peaks around the breeding season. The same pattern persisted in

travel time, with YOY spending more time travelling to patches than other demo-

graphic groups. Juveniles spent less time travelling to patches than other groups and

seasonal patterns were essentially the same as patch distance for all groups (Table 2).

17

Tab

le2.

Sum

mar

yof

beh

avio

ura

ldiff

eren

ces

from

linea

rm

ixed

-eff

ects

model

resu

lts.

The

colu

mns

for

Mal

esan

dF

emal

es(a

dult

s),

YO

Y,

and

JU

Var

eth

eex

pon

enti

ated

model

inte

rcep

tco

effici

ents

and

SE

for

each

ofth

e4

dem

ogra

phic

grou

ps

anal

yze

d(t

hou

ghnot

eth

atth

eF

orag

ing:

Tra

vel

coeffi

cien

tw

asbac

ktr

ansf

orm

edw

ith

the

logi

tfu

nct

ion).

The

Gro

up

Eff

ect

colu

mn

show

sth

ebas

icpat

tern

ofdiff

eren

ces

bet

wee

nea

chof

the

4dem

ogra

phic

grou

ps

(M=

adult

mal

es,

F=

adult

fem

ales

,Y

=Y

OY

,J

=Juve

niles

),th

ough

not

all

com

par

ison

sar

esh

own

(>=

0.05

leve

l;>>

=0.

01le

vel;>>>

=0.

001

leve

l).

Fin

ally

,th

eG

roup*S

easo

nco

lum

nin

dic

ates

ifat

leas

ton

eα

sign

ifica

ntl

yex

pla

ined

the

resp

onse

vari

able

for

atle

ast

one

dem

ogra

phic

grou

p.

Wher

ese

ason

aleff

ects

are

pre

sent,

they

are

bet

ter

under

stood

grap

hic

lyb

ecau

seof

the

up

to8

addit

iveα

’san

d4

dem

ogra

phic

grou

ps

toco

mpar

e.E

ven

wher

ean

nual

mea

ns

are

sim

ilar

(gro

up

effec

t),

the

seas

on*g

roup

inte

ract

ion

reve

als

diff

eren

ces

duri

ng

cert

ain

tim

esof

the

year

for

man

yof

the

resp

onse

vari

able

s(s

eeF

igure

s??,

9,an

d??).

p-v

alues

ondiff

eren

ces

bet

wee

nco

effici

ents

wer

edet

erm

ined

usi

ng

Wal

dte

sts.

*Only

wea

kse

ason

alpat

tern

was

found

and

only

inad

ult

mal

es.

Res

pons

eV

aria

ble

Mal

esFe

mal

esY

OY

JUV

Gro

upE

ffect

Gro

up*S

easo

nE

ffect

Dis

tto

Shor

e(k

m)

45.1±

4.3

39.6±

5.3

53.8±

8.2

29.9±

7.4

Y>

M=

F=

>J

p<

0.00

001

Wat

erD

epth

(m)

52.4±

4.0

38.9±

12.9

39.9±

13.7

41.5±

19.1

M=

F=

Y=

Jp

<0.

0000

1T

rip

Len

gth

(d)

7.4±

1.0

6.5±

0.65

8.6±

1.3

5.1±

1.1

Y>

M=

F>

Jp

<0.

0000

1P

atch

Dis

tanc

e(k

m)

28.2±

7.0

20.3±

3.6

40.2±

11.0

15.5±

7.15

Y>

M>

F=

Jp

<0.

0000

1Fo

ragi

ngR

atio

0.52

(.39−

.66)

0.85

(.39−

.89)

0.45

(.31−

.61)

0.55

(.34−

.75)

F>

>M

=Y

=J

p<

0.01

Pat

chR

esid

ence

(d)

4.3±

0.57

4.1±

0.39

3.9±

0.58

3.0±

0.72

Y=

F=

M=

Jp

<0.

01*

Tra

vel

Tim

e(d

)0.

70±

0.13

0.49±

0.06

0.92±

0.19

0.43±

0.12

Y>

M>

F=

Jp

<0.

0000

1T

rave

lSp

eed

(km

h−1)

1.53±

0.18

1.43±

0.12

1.63±

0.19

1.66±

0.34

Y=

F=

M=

Jp

<0.

01*

Tra

vel

Tor

tuos

ity

1.68±

0.25

1.48±

0.15

1.66±

0.23

1.57±

0.44

Y=

F=

M=

Jns

18

Adult females spent a greater fraction of each trip in the foraging state (foraging

ratio) than other groups year round. All groups tended to increase the amount of travel

during winter trips, but females did this to a significantly lower degree and winter trips

for females included much more foraging behaviour than other groups. All groups had

less travel and more foraging in the summer and more travel and less foraging during

winter. YOY had a tendency to spent more time travelling during the summer than

other groups (not shown).

Differences were not evident in patch residence, which averaged 3-4 days for

each group, year round (Table 2). This implies that seasonal differences in trip length

are primarily due to changes in travel time and patch distance, and not patch residence.

Discussion

We found a number of differences in habitat usage, foraging behaviour, and foraging

trip organization between YOY and adult grey seals. These differences have a number

of potential causes and implications for grey seals, the Scotian Shelf ecosystem, and

juvenile foraging behaviour.

A prominent feature of our results are the strong annual cycles of foraging

and movement behaviour in all age classes. Trip structure and habitat preference of

nonreproductive age classes suggest a summer nadir and winter peak in foraging effort.

Summer foraging trips were short, included little or no travel to shallow, near-shore

foraging areas, and more time spent hauled out between trips. Winter trips were long,

included no foraging near shore, more travel to more distant, deeper, off-shore foraging

areas. Haulouts between trips were short and infrequent, decreasing to less than 10% of

the time budget. Effort levels remained elevated into spring and early summer. Effort

decreased for the summer in Jul and began increasing again in Nov.

Using data from TDRs, Beck et al. (2003b,c) detected similar effort patterns

in diving behaviour, particularly when using accumulated bottom time as a proxy for

effort (see Fig. 5 Beck et al. 2003c). They surmised that decreased effort during summer

was due to timing lipid reserves with the breeding seasons so that adults did not carry

19

too much blubber too early in the year. Beck et al. (2003a) suggested that too much

blubber during the summer had negative repercussions on fitness. This hypothesis may

be partially true, particularly for adult females. However, YOY and juveniles follow a

similar pattern of lowered summer foraging effort. These young animals are growing

somatically and are extremely lean compared to adults, and avoiding too much blubber

cannot explain seasonal effort levels at this stage.

The timing of changes in foraging effort, however, is coincident with warming

and cooling of shelf waters which in turn drive the migrations of a variety of prey species

on the Scotian Shelf and nearby Gulf of Stain Lawrence (Perry and Smith 1994; Swain

et al. 1998). In the summer, warmer, more productive waters allow many temperature

sensitive demersal species to move into shallow waters to forage. Prey species move

close to shore and to the tops of shallow banks, including the bank upon which Sable

Island sits, bringing prey very close to the colony. The productive summer and early

fall months are also good feeding conditions for fish, and many fish species common

in grey seal diets increase lipid stores. Sand lance (Ammodytes spp), the single most

important prey item according to fatty acid analysis (Beck et al. 2007), feed over the

summer, increasing mass and lipid stores until they spawn in late fall (Smigielski et al.

1984). The combination of lipid rich prey close to haulout sites allow seals to decrease

foraging effort, while comfortably meeting metabolic needs and steadily gaining weight

through the summer and early fall.

Beginning in late fall and continuing through the winter, water cools and many

fish species move to deeper water along shelf edges and into basins on the shelf where

water temperature remains constant. Life history of some prey cause them to become

more difficult to capture during winter without migrating, and many species are in

poor condition by winter’s end. Sandlance lose 1/3 to 1/2 their mass during the late

fall spawning, then bury themselves in sandy substrates and enter a hibernation like

state for the winter months (Smigielski et al. 1984; Robards et al. 1999b). To forage

successfully during winter, seals must make longer trips to deeper, more distant areas.

They may also need to capture more individual prey items because winter prey are

lipid depleted and remain that way until zooplankton populations increase during the

20

spring phytoplankton bloom (Robards et al. 1999a; Kitts et al. 2004).

Seasonal changes in foraging behaviour are evident in all age classes, but the

timing of annual reproductive cycles superimposed over seasonal environmental con-

ditions produce a more complex pattern of foraging effort in adults. Like YOY and

juveniles, adults began the summer with a short period of increased foraging effort

just after the spring moult. By Jul, apparent foraging effort decreased considerably

and a large fraction of time is spent within 5 km of shore. Short trips continued until

Oct, when effort increased dramatically - about a month earlier than YOY or juveniles.

Adults may need to begin foraging earlier to ensure adequate energy reserves for the

energetically demanding breeding season which begins in Dec.

Adult foraging effort peaked in Nov, decreased during breeding, then increased

dramatically during winter. Though effort appears higher in winter, mass gain is

minimal. Adult males gain almost no mass and lose body energy, while females increase

mass only marginally (Beck et al. 2003a). The midwinter shelf environment appears

difficult compared to summer.

The seasonal pattern of effort differs somewhat between males and females, but

is very similar to the seasonal pattern of dive effort reported by (Beck et al. 2003c).

Beck et al. 2003c show that both sexes increase effort in the fall relative to summer,

but show that males put in a more consistent level of effort through the summer and

fall while female dive effort is reduced in the summer in increases dramatically in fall.

Trips are a different measure of effort than dives, and show a different pattern, with

males making a more dramatic increase in effort just prior to the breeding season.

Though reproductive life history differences are almost certainly contributing to effort

levels, males and females consume different prey due to size dimorphism. It is not

impossible that patterns of effort could be caused solely by differing seasonal life history

patterns of their respective prey. Spawning and wintering times of sandlance could be

a particularly important factor.

Conclusions. Here we demonstrate a number of ecologically significant behavioural

differences among two classes of adult and two classes of juvenile grey seals. Behavioural

state was determined using a state-space model. Although we could not ground truth

21

behaviours, all indications are that slow travel with many turns represents an increased

likelihood of foraging behaviour.

With SSM state estimations, we were able to divide foraging trips into foraging

and travel segments and analyze how trip organization differed between groups and

through the year. We found large seasonal changes in trip structure of all groups

that suggest increased foraging effort in winter and early spring and decreased effort

over the summer. These dynamics were likely driven by seasonal changes of prey

abundance and/or condition, though for adults the timing of reproductive investment

and expenditure superimposed over environmental cycles created a complex seasonal

pattern.

Finally, young of year showed greater foraging effort than other groups, including

longer trips with more travel to more distant foraging locations than older age classes.

These young animals appear to navigate to foraging areas as well as adults, but unlike

other groups, do not forage near the colony and instead spend more time traveling to

forage at locations away from the colony. This may be a predator avoidance strategy,

diet difference, or the competitive exclusion of YOY from foraging areas near the colony

as this large population experiences density-dependent population regulation after 40

years of sustained growth.

References

Anderson-Spreher, R., and J. Ledolter. 1991. State-Space analysis of wildlife telemetry

data. J. Am. Stat. Assoc. 86:596–602.

Austin, D., J. I. McMillan, and W. D. Bowen. 2003. A three-stage algorithm for

correcting ARGOS satellite locations. Mar. Mamm. Sci. 19:371–383.

Beck, C. A., W. D. Bowen, and S. J. Iverson. 2003a. Sex differences in the seasonal

patterns of energy storage and expenditure in a phocid seal. J. Anim. Ecol. 72:280–

291.

Beck, C. A., W. D. Bowen, and J. I. McMillan. 2003b. Sex differences in diving at

22

multiple temporal scales in a size-dimorphic capital breeder. J. Anim. Ecol. 72:979–

993.

Beck, C. A., W. D. Bowen, J. I. McMillan, and S. J. Iverson. 2003c. Sex differences

in the diving behaviour of a size dimorphic capital breeder: the grey seal. Anim.

Behav. 66:777–789.

Beck, C. A., S. J. Iverson, and W. D. Bowen. 2005. Blubber fatty acids of grey seals

reveal sex differences in diet of a size-dimorphic marine carnivore. Can. J. Zool

83:377–388.

Beck, C. A., S. J. Iverson, W. D. Bowen, and W. Blanchard. 2007. Sex differences

in diet reflect seasonal variation in foraging behaviour and energy balance: evidence

from quantitative fatty acid signature analysis. J. Anim. Ecol. 76:490–502.

Bowen, W., J. McMillan, and W. Blanchard. 2007. Reduced population growth of gray

seals at Sable Island: evidence from pup production and age of primiparity. Mar.

Mamm. Sci. 23:48–64.

Bowen, W. D., C. A. Beck, and S. J. Iverson. 1999. Bioelectrical impedance analysis

as a means of estimating total body water in grey seals. Can. J. Zool 77:418–422.

Breed, G. A., W. D. Bowen, J. I. McMillan, and M. L. Leonard. 2006. Sexual segre-

gation of seasonal foraging habitats in a non-migratory marine mammal. Proc. R.

Soc. B 273:2319–2326.

Breed, G. A., I. D. Jonsen, R. A. Myers, W. D. Bowen, , and M. L. Leonard. 2008.

Behaviour discriminating state-space analyses reveal foraging grounds and sex dif-

ferences in at-sea spatial behaviour of adult grey seals (Halichoerus grypus). Ecology

Accepted.

Doucet, A., N. D. Freitas, and N. Gordon. 2001. Sequential Monte Carlo Methods in

Practice. Springer-Verlag, New York.

Gelfand, A. E., and A. F. M. Smith. 1990. Sampling-based approaches to calculating

marginal densities. J. Am. Stat. Assoc. 85:398–409.

23

Iverson, S. J., W. D. Bowen, D. J. Boness, and O. T. Oftedal. 1993. The effect

of maternal size and milk energy output on pup growth in grey seals (Halichoerus

grypus). Physiol. Zool. 66:61–88.

Jonsen, I. D., J. Mills-Flemming, and R. A. Myers. 2005. Robust state-space modeling

of animal movement data. Ecology 86:2874–2880.

Jonsen, I. D., R. A. Myers, and M. C. James. 2007. Identifying leatherback turtle

foraging behaviour from satellite telemetry using a switching state-space model. Mar.

Eco. Prog. Ser. 337:255–264.

Jonsen, I. D., R. A. Myers, and J. Mills-Flemming. 2003. Meta-analysis of animal

movement using state-space models. Ecology 84:3055–3063.

Jouventin, P., and H. Weimerskirch. 1990. Satellite tracking of wandering albatrosses.

Nature 343:746–748.

Kalman, R. E. 1960. A new approach to linear filtering and prediction problems.

Trans. ASME J. Basic Eng. 82:35–45.

Kitts, D., M. Huynh, C. Hu, and A. Trites. 2004. Season variation in nutrient compo-

sition of Alaskan walleye pollock. Can. J. Zool 82:1408–1415.

LeBoeuf, B. J., D. E. Crocker, D. P. Costa, S. B. Blackwell, P. M. Webb, and D. S.

Houser. 2000. Foraging ecology of northern elephant seals. Ecol. Monograph. 70:353–

382.

Lidgard, D. C., D. J. Boness, W. D. Bowen, and J. I. McMillan. 2005. State-dependent

male mating tactics in the grey seal: the importance of body size. Behav. Ecol.

16:541–549.

McConnell, B. J., C. Chambers, K. S. Nicholas, and M. A. Fedak. 1992. Satellite

tracking of grey seals (Halichoerus grypus). J. Zool. 226:271–282.

Newman, K. B. 1998. State-space modeling of animal movement and mortality with

application to salmon. Biometrics 54:1290–1314.

24

Perry, R., and S. Smith. 1994. Identifying habitat associations of marine fishes using

survey data: An application to the northwest Atlantic. Can. J. Fish. Aquat. Sci.

51:589–602.

Prince, P. A., A. G. Wood, T. R. Barton, and J. P. Croxall. 1992. Satellite-tracking

wandering albatrosses (Diomedea exulans) in the South Atlantic. Antarct. Sci. 4:31–

36.

Robards, M., J. Anthony, G. Rose, and J. Piatt. 1999a. Changes in proximate com-

position and somatic energy content for Pacific sand lance (Ammodytes hexapterus)

from Kachemak Bay, Alaska relative to maturity and season 242:245–258.

Robards, M. D., M. F. Willson, , R. H. Armstrong, and J. F. Piatt, 1999b. Sand lance:

a review of biology and predator relations and annotated bibliography. Technical

Report Res. Pap. PNW-RP-521, U.S. Department of Agriculture, Forest Service,

Pacific Northwest Research Station., Portland, OR.

Shaffer, S., Y. Tremblay, H. Weimerskirch, D. Scott, D. Thompson, P. Sagar, H. Moller,

G. Taylor, D. Foley, B. Block, et al. 2006. Migratory shearwaters integrate oceanic

resources across the Pacific Ocean in an endless summer. PNAS 103:12799–12802.

Sibert, J. R., M. K. Musyl, and R. W. Brill. 2003. Horizontal movements of bigeye

tuna (Thunus obesus) near Hawaii determined by Kalman filter analysis of archival

tagging data. Fish. Oceanogr. 12:141–151.

Smigielski, A., T. Halavik, L. Buckley, S. Drew, and G. Laurence. 1984. Spawning,

embryo development and growth of the American sand lance Ammodytes americanus

in the laboratory. Mar. Eco. Prog. Ser. 14:287–292.

Stewart, B. S., S. Leatherwood, P. K. Yochem, and M. P. Heidejorgensen. 1989. Harbor

seal tracking and telemetry by satellite. Mar. Mamm. Sci. 5:361–375.

Swain, D., G. Chouinard, R. Morin, and K. Drinkwater. 1998. Seasonal vari-

ation in the habitat associations of Atlantic cod(Gadus morhua) and American

25

plaice(Hippoglossoides platessoides) from the southern Gulf of St. Lawrence. Can.

J. Fish. Aquat. Sci. 55:2548–2561.

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