J . Zool., Lond. (1996) 240, 175-200

The diel hauling-out cycle of harbour seals in an open marine environment: correlates and constraints

PETER WATTS

Zoology Department, University of GuelphlFisheries Center, University of British Columbia, Canada

(Accepted 27 September 1995)

(With 8 figures in the text)

A colony of harbour seals in the Pacific north-west was monitored over two years, concurrent with a variety of environmental variables. Regression models described die1 hauling-out activity as: i) a photoperiodic cycle; ii) a function of other environmental factors; or iii) a cycle modified by environmental constraints. Throughout the year, the number of seals on shore followed a diel pattern with a midday peak. Seals hauled-out in lower numbers in winter than in summer, and for a smaller proportion of the day (although for about the same proportion of the photoperiod). During the annual moult, numbers hauled were elevated around the clock, and the midday peak was skewed to late afternoon/early evening. Models that defined hauling-out in terms of environmental factors were significant, but did not fit the data as well as models based on photoperiod. The strongest environmental correlates (such as tidal height) owed much of their explanatory power to artefactual similarities with the photoperiodic cycle. Four general conditions are presented which, if met, should always result in a die1 hauling-out cycle with a midday peak. The most fundamental of these involves a proposed ‘cost of immersion’ which motivates pinnipeds to haul-out when not foraging. Two likely candidates for such a cost involve risk from aquatic predators and the energetic expense of sleeping while immersed.

Introduction

All pinnipeds ‘haul-out’ on to land or ice. Although some species do this only to reproduce and moult, others (such as harbour seals, Phoca vitulina) haul-out throughout the year. Beyond the obvious constraints of moulting and reproduction, the underlying reasons for this behaviour are poorly understood. Among phocids, hauling-out has been attributed to sleep (Schneider et al., 1980), grooming (Sullivan, 1979), and even Vitamin D synthesis (McLaren, 1958), but none of these hypotheses has been tested. Harbour seals are said to haul-out to ‘bask’ ( e g Boulva & McLaren, 1979; King, 1983; Lavigne & Schmitz, 1990), but again there is no real evidence for this (Venables & Venables, 1955). In fact, prolonged basking during the temperate summer may cause seals to overheat (Watts, 1992).

Harbour seals often haul-out following a die1 cycle in which numbers on shore peak near midday and are lowest at night (e.g. Allen et al., 1984; Stewart, 1984; Pauli & Terhune, 1987a; Yochem et al., 1987; Godsell, 1988; Thompson & Miller, 1990). Studies that explicitly report such a pattern (if they discuss it at all) attribute low night-time counts to nocturnal foraging (e.g. Boulva & McLaren, 1979; Hansen, 1979; Haaker, Parker & Henderson, 1984; Stewart, 1984; Allen, 1989); although plausible, this begs the more basic question of why seals should come ashore when they are not foraging.

A number of other studies have explored the effects of various environmental factors on 175

1996 Zoological Society of London

I76 P. WATTS

hauling-out behaviour. Tidal height. air temperature, wind speed, wave action, time of year, precipitation. physical disturbance. and even moonlight have all been correlated with the number of seals on shore (Table I). Aside from such obvious constraints as tide and disturbance, few of these efTects have been considered in studics of die1 hauling-out cycles. Such effects might deform or mask an inherent pattern: this may account for cases in which midday peaks in the number of seals on shore occur only at certain times of year (e.g. Thompson er d.. 1989), or not at all (e.g. Heide-Jorgensen. 1979: Sullivan. 1980; Renouf t f (d. . 1981: Thompson e f ( I / . , 1989).

To date, few studies have explored in detail both environmental and die1 facets of hauling-out behaviour. Author% tend to present hauling-out as either a simple die1 cycle (e.g. Schneider & Payne. 1983; Stewart. 1984) or as a suite of environmentnl correlates (e.g. Krieber & Barrette, 1984: Pauli & Tcrhune. 1987h). Authors who do use both approaches have limited themselves to basic tests of significance for ii few variables such as tide or time of day (e.g. Pauli & Terhune, 1 9 8 7 ~ : Thompson & Miller. 1990). Studies which explicitly model die1 cycles of hauling-out are rare (but see Erickson. Bledsoe & Hanson. 1989): those that also incorporate environmental factors are even more so.

Much recent work has relied on the use of radio telemetry and time-depth recorders to provide detailed information on the hehaviour of individual animals. While this generates valuable insights into the habits of small numbers of seals, and even on specific subclasses (e.g. lactating females- Thompson r ’ r ( I / . . 1994). individual behaviour can be variable at the best of times (e.g. Pitcher & McAllister. 1981; Thompson et i l l . , 1989). Moulting and lactation further complicate hauling-out patterns (Boness. Bowen & Oftedal, 1994: Thompson t’t d.. 1994). Variation within small numbers of individuals can, therefore, mask general trends more clearly seen in larger groups. The small sample sizes associated with many telemetry studies make them particularly vulnerab~e to this relationship between statistical significance and N. Yochein et ctl. (1987). for example. reportcd that seven of 17 radio-tagged harbour seals at Sail Miguel Island (California) preferentially- hauled-out during darkness. although the colony as a whole showed a midday peak in numbers hauled-out (Stewart. 1984; Yochem et ul., 1987).

This paper describes an attempt to quantify both the die1 hauling-out cycle and its environ- mental constraints. for ;L colony of harbour seals in the Pacific north-west. I compare the fit of three regression equations to census data. The first of these describes the number of seals on shore as a function of various environmental factors. the second as a purely die1 (photoperiodic) cycle. and the third as ;i die1 cycle modified by other environmental effects. Finally, 1 speculate on four logical prerequisites for the persistence of a midday peak in numbers hauled-out, which should apply to pinnipeds in general.

Study area

The study took place at Snake Island (49’ 72’N. 124”36’W), a typical harbour seal haul-out site in the southern Strait of Georgia. Snake Island is about 1 km long and 100-200 in wide, and lies in open water (60- 120 m deep) with fairly uniform bottom topography (Canadian Hydro- graphic Service, 197 1) . Rocky reefs surrounding the island provided terrestrial habitat for at least 200 harbour seals during the study. Owing to the small size and relative isolation of Snake Island (4 kin from the nearest island), animals on land were in no danger from terrestrial predators, althoush they were commonly disturbed by boat traffic during the summer. Aerial censuses of h u / - o u ~ sites in this region have generally been done during summer low tides, since the greatest number o f animals was thought to be ashore at such times (Olesiuk, Bigg & Ellis, 1990~). Such

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I78 P WATTS

condi t ions often occur near midday. however, so it is unclear whether the peak is more reflective of an inherent die1 cycle. or an opportunis t ic exploitation of intertidal Izaul-out area. Seals at Snake Island appear t o be nocturnal foragers (Watts. 1993), and can be constrained from hauling-out in hot summer weather (Watts . 1992).

Methods

Field nietIior!ologj~

Datrt were collected from the summer of 1986 to the spring of 1988. Sampling occurred in periods of 4-9 days at approximately monthly intervals throughout the year. The number of seals on land was counted at half-hourly intervals throughout the day. concurrent with a variety of environmental variables. During the night, the same observations were made every 1 to 3 hours. Night counts were made using a passive vision starlight amplification system (Smith & Wesson. 'Star-Tron' Model 303ABN).

Tidal height (m) at each count was calculated using a cosine function applied to slack-tide predictions from government tide tables (Canadian Hydrographic Service. 1986, 1987, 1988). Precipitation was measured hourly using ii rain gauge calibrated in mm. Solar radiation was measured (Model R401 solar radiation recorder. Weather Measure Corp. Sacramento. CA) only during 1987-88. Wind speed was measured 2 m above ground level using a hand-held anemometer ('Wind Wizard', Davis Instruments, Hayward. California). Air temperature was measured using a mercury therniomcter, shielded from the effects of sunlight and wind, I m above ground level.

Solar radiation. wind speed, and air temperature all contribute to the perceived thermal environment a t any time. (For example. a cloud blocking the sun will generally make one feel 'colder', although air temperature does not change.) However. their effects are not simply additive; a comprehensive regression model should include interactions between variables. I therefore incorporated these 3 measurements into a composite variable called flu.\- (F , ) , an index of heat exchange (in Watts. m-') between a harbour seal and its environment. F h v is derived in Watts (1992) and is defined as:

where (I,,,, and o,/,) are the absorptivities of the seal's surface to shortwave and longwave radiation, respectively: S is measured solar radiation (WnC'): :(, and E, are atmospheric and surface longwave emissi\ ity. respecti\.c.ly; (T is the Stefan-Boltzmann constant (5.673, 10-'Wm-2 T,, and T, are ambient air and radiant surface temperature ('C). respectively: and 1' is wind speed (ms-'). Positive F, implies net heat gain by the seal. while negative F, implies net heat loss. Nus describes interactions of insolation, wind speed. 'ind air temperature niore realistically than do the arbitrary multiplicative interaction terms generally eniplo! ed in regression analysis.

The site was defined as 'disturbed' if some identifiable stimulus caused at least 5% of the hauled-out aninial< 10 re-entcr the water. Typical disturbances included boat traffic (especially during the summer month.;). and lieaiy rain squalls (during the winter). Readings taken within an hour of a disturbance were excluded from analysis: disturbed seals generally reemerged within this time.

All >tatistical and graphical analysis of the field data was performed using SYSTAT/SYGRAPH software. release 5.03 (Wilkinson. 1990).

Triiiisfornicition qf census rhta

At any given site. seasonal variation in numbers hauling-out can result from local immigration and emigraiion (Thompson. 1989; Thompson c / d.. 1989). although other, non-migratory changes in behaviour ma! also play ;I role (Boulva & McLaren. 1979). Harbour seals are also known to spend extended periods

DIEL HAULING-OUT CYCLE OF SEALS 179

ashore when nursing, and during the annual moult (e.g. Bouha & McLaren, 1979; Brown & Mate, 1983). In light of these expected behavioural differences, the data were split into categories, based on major seasonal differences in the maximum number of hauled-out seals. The data from each category were analysed separately.

Even within seasonal categories, the number of harbour seals using Snake Island typically changed between visits. This could have occurred for a t least 2 reasons; the local group could have changed the amount of time they spent on shore, or the actual size of the group could have changed due to immigration and emigration. To minimize the confounding effects of emi/immigration, and to permit comparison of data taken during different visits, seal counts were expressed as a proportion of the maximum count for the visit in which they were recorded. For example, a count of 25 seals taken during a visit whose maximum count is 50 produces a standardized value of 0.5. This is equivalent to assuming that changes in counts during each sampling period were due to changes in the behaviour of the animals, and that all significant net immigration and emigration occurred between sampling periods. Note that this does not imply that the Snake Island site is in any way 'closed' to immigration or emigration, only that both are effectively in balance over the course of a visit. This is consistent with the known site fidelity of harbour seals, both generally (Pitcher & McAllister, 1981; Yochem et a/., 1987; Thompson & Miller, 1990) and in the Pacific north-west (Suryan, 1995).

Once standardized, the proportions were transformed (arcsine square-root) to satisfy assumptions of normality and homogeneity of variance (Zar, 1984). This transformed quantity is referred to as '/zuu/- out' for the rest of this paper. All detailed analyses were performed using haul-our as the dependent variable.

Regression model I: diel c

Any equation which describes a purely die1 cycle superficially describes behaviour over time. The model presented here expresses time in terms of photoperiod. This reflects the nocturnal foraging habit of the seals at Snake Island, and the increased near-surface availability of vertically-migrating prey at night (e.g. Harden Jones, 1968; Hansen, 1979). (Harbour seals can forage at the greater depths where prey reside during the day, but less efficiently, since a greater proportion of their time underwater must be spent in transit; this reduces effective foraging time.) In essence, then, the model assumes that the hauling-out cycle is not an intrinsic function of time per se, but is instead (like other diel cycles) driven by changes in ambient light level.

The independent variable is therefore photoperiodic time, or t . It derives from solar elevation, which is in turn a function of latitude, Julian day, and time of day (Campbell, 1977: 55) . A 'day' begins when the sun rises past an elevation angle of - 18 (the onset of astronomical twilight) and ends when it sinks beneath the same angle. The length of this period is arbitrarily set to 1, so that during the photoperiod, 0 < t < 1 , and i = 0.5 at solar noon. Between nightfall and midnight, t > I ; between midnight and the onset of morning twilight, t < 0.

Each day, therefore, is standardized so that any given value of t represents the same point in the photoperiod, regardless of the time of year. (In contrast, most points on the conventional 24-h time scale are associated with different points in the photoperiod at different times of year.) This permits comparison of the data between seasons.

Since t is designed to express the proportion of a given photoperiod that has passed, it is not a consistent quantity outside the range 0 < t < l(corresponding to the darkest hours centred on midnight). Detailed analyses were limited, therefore, to data collected at solar elevations greater than - 18 '. This contained virtually all the observed variation in numbers ashore.

Given a photoperiodic measure of time, it is straightforward to derive an equation which represents the die1 cycle for seals hauling-out. Assuming nocturnal foraging, the number of seals on shore at any time during the day is simply the number that have hauled-out after one night, minus the proportion of that group which has departed to forage the next. Both components can be conveniently described as sigmoid curves. Thus, the number of seals which have arrived on the site by any t ( A , , expressed as haul-out) follows

P. WATTS

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D l E L H A U L I N G - O U T CYCLE OF S E A L S 181

the form:

where c ' represents the number of seals already on land just before dawn (expected to be low); 111 is the total increase in numbers hauled-out (the amplitude of the sigmoid curve); i,,, is photoperiodic time when A , = 0.5m (the inflection point ofthecurve, when arrival rate is maximal): and rr represents orrival rate at the lrmrl-orrt site (the slope steepness around ia).

The number of seals that have departed at time t (D,) can be described as another sigmoid function, the amplitude of which is determined by A, :

where in is the photoperiodic time when half the departing seals have left; and d represents the rate of departure from the lzrrul-out site.

The number of seals on shore (N , ) at any t between 0 and I , then, is simply

N , = A , - D , , O , < t , < 1 (4) Equation (4) can be used to describe a hauling-out pattern widely reported in the literature (e.g. Boulvn & McLaren, 1979; Schneider & Payne, 1983; Allen et id., 1984; Stewart, 1984; Godsell. 1988). assuming only that ii single major peak in numbers hauled-out occurs during daylight, and that arrivals and departures at the site follow approximately normal distributions (implicit in the use of the sigmoid equation).

Non-linear Icast-squares regression was used to fit equation (4) to the census data. Although these data were taken across many days, counts made close to each other on the same day were obviously not independent. This would seem to violate an assumption of regression analysis (although least-squares techniques are robust to most such violations) (Zar, 1984). in this analysis. however, time itsclfis the independent variable; the model does not ignore potential time-dependence, but explicitly incorporates it into the prediction.

While fitting data to this model, the values of c and 117 were constrained so that neither parameter. nor their sum, fell below 0 nor exceeded I .6 (the arcsine-square-root transfornis of 0 and I , respectively). This precluded 'nonsensical' regression coefficients (such as fewer than zero seals hauled-out), even if they would have iinproved the fit of the model.

Probability plots were used to ensure that the regression residuals were normally distributed (Zar, 1984). The residuals from each regression model were also examined for heteroscedasticity and nonrandomness. To determine whether the models were appropriate to the data, scatterplots of residuals vs. r were fitted using LOWESS, a nonparametric locally-weighted regression (Cleveland. 198 I ) . Appropriate models generate random residual scatters whose trend lines are relatively straight, with slopes and yintercepts near zero (Zar. 1984; Wilkinson, 1990). When a model fails to include important variables, however, the scatter of its residuals is not random. reflecting the pattern of variance which would be explained by the missing variables.

To quantify significant differences in model fit over time, the residual data were also grouped into discrete time intervals and subjected to analysis of variance. I repeated the analysis after breaking the data into 3. 4. and 5 categories (0.33, 0.25, and 0.2 units wide, respectively). This was done to avoid any spurious results arising from arbitrary category groupings. Since these analyses all produced similar results, only those from the 5-treatment ANOVA are presented below.

Rqyrssion model II: emir-onniental cflYi'cts

To permit comparison with the signioid model, the environmental regressions were also limited to data collected when solar elevation was > ~ 18" (see above). Scatterplots of htrzd-oirt vs. various environmental factors showed relationships which were. despite a great deal of scatter, consistent with a linear model for the bulk of the data (Fig. I ) . I therefore used interactive stepwise multiple linear regression to define ht r~d-o~r t

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Pro ba 1% I i t > - p I o t : in d resi d ua I it na 14 ses of the en vi ron men t ;i I ni ~i 1 t i I i nea r m odcls wcre performed as ubu\ c.

D l E L H A U L I N G - O U T CYCLE OF SEALS

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FIG 3. Daily maximum number of seals hauled-out a t Snake Island throughout the year

regressions which simultaneously estimated the coefficients of the die1 and environmental components:

s, = lVr,<, + alxl + a2x2.. . + u,s,,, 0 < f < 1 ( 5 ) where N,,(> is the same photoperiodic model described in equation (4). but derived as part of a larger expression containing explicit environmental factors (XI, .Y.. . . x,J. u,, 02 , . . . u,, are the respective regression coefficients of these factors. (The y-intercept, c, is a component of N,,<,; see equation (4).) Probability plots and residual analyses followed the methodology described for the other models.

Results

Both the maximum number of seals on shore and the shape of the die1 hauling-out cycle itself varied significantly throughout the year (Figs 2, 3). The data were divided into three broad seasonal categories to reflect this variation: winter, summer, and autumn (which corresponded to the annual moult). Note that these names are descriptive, and do not correspond exactly with a calendar definition of the seasons.

Throughout the year, the greatest number of seals hauled-out during the early afternoon. In winter (Julian day > 300, or Julian day < 100) that maximum was always less than 75 (Fig. 3). Few animals hauled-out at night, with the exception of a relatively minor peak in numbers on shore between midnight and 06:OO h; this peak declined with the onset of morning twilight (Fig. 2).

During summer (Julian days 101 to 250), the maximum number of seals on shore varied from 50 to 150 (Fig. 3). This was accompanied by a pronounced increase in the amplitude of the die1 cycle; fewer seals hauled-out at night than during winter, and more hauled-

P WATTS

DIEL H A U L I N G - O U T CYCLE O F SEALS

5 7 9 11 13 15 17 19

Time of day

FIG 5 . Sigmoid regressions of haul-out vs. time of day for summer and winter. Shaded areas denote the 95% confidence intervals for arrival and departure curve inflection points.

out during the day. In the Strait of Georgia, the pupping season extends from midJune to mid-September (Bigg, 1969), but maximum numbers of seals on shore during that interval were not significantly higher than during the rest of summer (Mann-Whitney U = 173, P = 0.230).

Autumn (Julian day 251 to 300) was characterized by high numbers of harbour seals hauled- out around the clock, although diel maxima (130 to 180 animals) (Fig. 3) still occurred during the afternoon (Fig. 2). Seals stayed ashore in greatest numbers during this time of year, although increased nocturnal hauling-out also served to reduce the amplitude of the diel cycle relative to summer. A few days after the end of autumn, the maximum number of seals had declined to characteristic winter levels.

When plotted on the standardized photoperiodic time scale, the widths of the summer and winter early-afternoon peaks appeared to be very similar (Fig. 4). Since the winter photoperiod is shorter than that of the summer, however, this implies that the winter peak was actually narrower in real time. This was in fact the case (Fig. 5). When the diel regression model was fitted using time of day rather than t , the summer estimates of iA and iD (midpoints of the arrival and departure curves, respectively) were significantly different from the corresponding winter estimates (Table 11, Fig. 5) .

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The sigmoid model accounted for 32 to 50% of the observed variation in Imil-out (Tablc 111). It performed best \vhen Lipplied to the suriitiier data, and worst in describing h o d o u t during winter. The parameter estimates of the suininer and winter data sets produccd a function with a roughly symmetrical peak ceritrcd on early afternoon; those of the moult data, however. suggested a peak with ;in elevatcd baseline ( h ) and rcduced amplitude. skewed strongly towards late afternoon earl) evening (Fig. 6. Table IV).

Residual scatterplots showcd no pattern for the winter and autumn data. Thc LOWESS sniootli for all three seasons \vas relatively flat and straight with an intcrcept near zero (Fig. 7 ) ,

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D I E L H A U L I N G - O U T CYCLE OF S E A L S I87

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188 P . W A T T S

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Sieinoid regression\ Winter 0 . 3 I8 1.287 4.318 0.514 5.501 0.757 Summcr i).33x 1.262 0.390 0.445 7.832 0.746 Autumn 0.726 0.412 12.688' 0.545 12.414 1.018

Coinposite regrewon\ Winter 0.502 I .o9x 5. I79 0.524 4.365 0.799 Suininci- 1.018 0.571 4.037, 0.369 4.307 1.016 Aut urnn 0.520 0.374 14.121 0.5 50 12.902' 0.997

' 9 5 " ~ confidence limits for (I in the signioid regression ( - 1.93 1 to 27.306). and for 11 (-3.791 to 32.033) and d-0 .086 to 25.889) in the composite I-egresiun. shoa that these parameter cstimates are not significantly different from zero

and there were no significant differences between residuals at different times of day (P 3 0.89 I Table V). The sigmoid model. therefore. appears to be an appropriate one. However, the summer residuals showed greater variation during late morning and late afternoon than at other times. Although the model closely predicts the apex and the trough of the summer die1 cycle, its description of the intervening rates of change is not as good.

Multilinear regressions of liciuI-ouf against environmental factors accounted for from 10% (autumn) to 5 8 % (summer) of the observed variation (Table 111). Unlike the sigmoid regressions, these predictions were not a pure function of photoperiodic time, and showed scatter when plotted against t (Fig. 6). The environmental model surpassed the signioid model when fitted to the summer and winter data, but produced a poorer fit on the autumn data (Tablc 111). Virtually all of the environmental correlation in the summer regression was due to tidal height; a simple linear regression of summer IuuiI-out on tidal height produced an r(,(/,.' of 0.543, compared to only 0.040 and < 0.001 during winter and autumn. respectively (Table VI).

H(iirl-out was correlated (negatively) with tidal height only during summer. Air temperature and hcorl-out were positively correlated during all three seasonal periods (Table VII). Insolation was a positive correlate of hrrul-out during winter and summer, but was not significant during autumn. Flux was correlated with htnrl-out only during the winter.

For winter and summer data. the environmental model tended to underestimate h d - o u t near midday. while overestimating i t near t = 0 and t = 1 . During autumn, however, the environ- mental model underestimated /u/u/-ouf late in the day. In all three cases, the residuals produced by the environmental regression are not random across f . displaying a midday peak similar to the sigmoid die1 cycle (Table V; Figs 6. 7). These residual peaks were highly significant during sumnicr' and autumn ( P < 0.010). and nearly significant during winter ( P = 0.080) (Table V).

T\i<ti . V

I .A .YO I .'.l P-vt//L/l'.\ i .$i~llifll~i/llli' O/ r/ i l ' / l ' l~ . s i l~ f r t r i ~ ? ~ i f f l ' ~ l l . \ 17) ' ' ~ l ~ t l . \ l ~ l l ' / 0 1 ' r t rc 'h l l l f J ( i ~ ~ /

Model Winter Summer Autumn ~~ ~ __.__

\igmoici 1 .ooo 0.891 0.984 en\ ironincntal 0.080 0.001 <0.001 coinposite 0.998 0.899 0.981

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wi 1IlcI- ( t 04.; -0.103 170 0.040 0.367 0.005 Sumlne1 I .4tc 1 - 0 , 3 0 3 34 7 0.53.3 0.273 < 0.00 I 14 111 lllllll 11.8 I s O.Ol4 155 <: 0.001 0.273 0.550

Combining the sigmoid and multilinear equations (eqn 5 ) generated predictions that strongly resembled the time-dependent signioid regressions in shape. but which also showed some scatter in the estimates a t an) yiven t due to environmental effects (Fig. 6). This was only a slight improvement ober the best of the alternative models for summer (environmental) and autumn (sigmnid), surpassing their ~',,,~,'s by 4 and 3%. respectively (Table 111). The composite rc,c/i.2 for winter. howevcr. ivas 9';'" higher than that of the best alternative (environmental). There was no significant dif'firence betLveen composite and sigmoid residuals for any season; the LOWESS smooths uere virtuall!, identical (Fig. 7). and there were clearly no significant differences between residuals at difttrent times of d a y ( P 2 0.899- -Table V).

Thermal variables tended to be less important in the composite equations than in the purely environmental ones. Air temperature lost its significance during winter. while both air tempera- ture and insolation becmie insignificant during summer. in the presence of (Tables V. VI I ) .

Discussion

Seo \ o l l l r l l l ~ C ~ I l d \

The Snake Icland data (Fig. 2 ) are consistent with other reports of increased numbers of seals on h ! - e during the moult (Boulva & McL'tren. 1979: Johnson & Johnson, 1979; Brown & Mate.

DIEL H A U L I N G - O U T C Y C L E OF SEALS

1983; Stewart & Yochem, 1984). The present study further establishes that this increase applies around the clock, implying either a larger local population or a greater overall reluctance to enter the water. Moulting in harbour seals may be associated with a metabolic depression of 15-20% (Ashwell-Erickson et al., 1986), which would lead to reduced food requirements. In addition, the peripheral tissues may receive increased blood flow during the moult; this would make thermoregulation more expensive for an immersed animal. Either factor might be expected to increase the amount of time a seal would spend hauled-out (see Ling, Button & Ebsary, 1974).

More surprising was the lack of any significant increase in numbers hauled-out during the pupping season. The moult and the breeding season are frequently reported to be the times when the greatest proportion of the population is hauled-out (e.g. Rosenthal, 1968; Johnson & Johnson, 1979; Fancher, 1979; Allen & Huber, 1983; Calambokidis, Steiger & Healey. 1983: Stewart & Yochem, 1984). Since pups (and occasional births) were observed at Snake Island during the breeding season, the lack of an increase in haul-out at this time cannot be easily attributed to the sort of sexual segregation observed at some other sites (Jeffries, 1982; Seater & Markowitz, 1983; Allen, Ribic & Kjelmyr, 1988; Thompson, 1989).

Although summer and winter diel patterns were similar, fewer seals hauled-out at any given time during winter. Whether this represents seasonal changes in hauling-out behaviour (as suggested by Boulva & McLaren, 1979, for Atlantic harbour seals), or net emigration away from Snake Island in winter is not known. Olesiuk et ul. ( I 9906) suggested that the seal population of the Strait of Georgia is actually highest in winter (owing to the autumn birth of pups), and that there is considerable variation in seasonal trends even among local estuaries and islets; however. they failed to mention how their estimates were derived, or whether those estimates referred to hauled-out or total number of animals. Harbour seals off the coast of California. at least, IiuuI-out less frequently in winter than at other times of year (Stewart & Yochem. 1989).

Not only did seals haul-out in lower numbers during winter, the duration of the diel peak declined as well. Although the widths of the summer and winter peaks are similar on a photoperiodic scale (Fig. 4), there is an obvious and significant difference when the data are plotted against time of day (Fig. 5) . This is consistent with the premise that the hauling-out cycle is essentially a function of photoperiod.

Seasonal movements of harbour seals are frequently correlated with changes in prey avail- ability (e.g. Fisher, 1952; Beach, 1981; Jeffries, 1982; Brown & Mate, 1983; Roffe & Mate, 1984). In the waters around Snake Island, Pacific hake (A4erlucciu.s productus) dominate the harbour seal diet during summer, while during winter the vast majority of prey are herring (Clipru hur.mgu.v) (Olesiuk et ul., 1990h). Whether this has any relevance to hauling-out behaviour ~~

whether, for example, changes in foraging time for different prey types changes the amount of ‘free time’ spent on shore--remains an open question.

191

Regressioii models

The pure environmental model produced slightly higher rc,c1i,2s than the pure sigmoid when applied to the summer and winter data, and a much worse fit when applied to the autumn data (Table 111). However. the significant residual deformation (Table V, Fig. 7) implies that this model is incomplete, failing to account for a pattern of variation that is reminiscent of the sigmoid equation. In contrast, the residuals produced by the sigmoid regression show no such significant patterns.

Regressions that combined sigmoid and environmental factors always produced the highest

I91 P . WATTS

r<,(/,.'s, coupled with a random residual scatter (Table 111, Fig. 7). Thermal variables tended t o be less important in the composite equations than they were in the purely environmental ones. Insolation (during summer) and air temperature (during summer and winter) lost their importance in the presence of the sigmoid component, while N,,,, remained significant. This suggests that the significance of thermal factors in the environmental models may have been ;it

least partly a n artefact of covariance with photoperiod. Since temperature and insolation tend to covary with solar elevation, purely photoperiodic effects would likely correlate with them in the absence of an explicit photoperiodic variable.

A similar covariance may also extend to tidal height during summer (which was the only seasonal category in which this factor was significant). Although three variables were significant in the summer environmental regression, virtually all of the explanatory power was due to the tidal component (Table VII). Lowest tides and maximum values of lznul-out both occurred i n early afternoon during summer. However, a similar peak in the number of seals on shore persisted at other times of year as well, although the lowest tides generally occurred at night during winter and autumn (Fig. 8). There was, in fact, no significant tidal effect outside the summer months (Table VIII). This makes it very likely that the significance of tidal height during summer was mainly due to its coincidence with the peak of a die1 cycle, rather than to any direct limitations imposed by high water levels.

Hauling-out among harbour seals at Snake Island, therefore, is more consistent with ii

photoperiod-based die1 cycle than with the other environmental factors considered.

1 .E

1.2 c 3 0 3 0.E s

0.4

0.c 0 6 12 18 24 0 6 12 18 24

Time of day

I I I , I I I

F i C i 8. LOWESS-smoothed scatterplots oE tidal height and l i d - o u t vs. time of day for siiminer and lion-summer data ( F - 0.3).

Regression Variable* coefiicien t S.E. Tolerance** P

Wintcr N, < I -~ 6.00. 1 O--' 0.135 < 0.001 flu x 2.90.10 0.016 0.223 < 0.001 i 11 sola t i o n 7.16. 10 ' 2.2, 1 0 P 0.380 0.001

Sumincr )V,,<, 0.026 0. I58 < 0 00 1 tide -0.201 0.0 10 0. I58 < 0.00 I

Aut tinin ,v,,c - 0. I20 0.418 < 0.001 air "C 0.017 0.006 0.41x < 0.001

* Listed in decreasing order of signilicance * * I - r of variable against other variables

Tfir sigmoid peak

Since even the composite models account for (at best) only 60% of the observed variation i n Izuul-out, other fx tors must also exert significant effects. None the less, hauling-out at Snake Island clearly followed a diurnal cycle with a peak near midday. Throughout the following discussion, this general pattern is referred to as a 'sigmoid peak'.

Sigmoid peaks are common at pinniped colonies not overwhelmingly constrained by such things as tide or human activity. Among harbour seals, they have been widely reported from both Pacific (Hoover, 198 1 ; Calambokidis, Steiger & Healey, 1983; Allen Pf al., 1984; Stewart, 1984)

sometimes even in the presence of strong tidal constraints (Pauli & Terhune, 1987~) . Nor is this unique to harbour seals. Ringed seals Phoca hispida ( Smith, 1973; Finley, 1979), California sea lions Zaloplius cdifomianus (Mate, 1975), and the Antarctic phocids (see Erickson rit a/., 1989) all /rrru/-ouf following the same general pattern.

Aftcr controlling for such constraints as tide and disturbance, a sigmoid peak should persist only if four conditions are met:

and Atlantic (Boiilva & McLaren, 1979; Schneider & Payne, 1983; Godsell, 1988) popul, d t ' ions.

1 ) seals have time to haul-out if they wish (i.e. they need not forage continuously to meet their

2) Immersion carries some cost which motivates seals to leave the water when not

3) Foraging payoffs are greater during darkness than daylight, i.e. there is a nocturnal peak i n

4) The costs of commuting (locomotion and potentially reduced foraging opportunities) are

energetic needs);

foraging';

prey availability; and

lower than those of remaining at sea between foraging bouts.

"Cost' hcrc is used to describe a n y loss in the 'lifetime fitness function' as defined by Mangel & Clark (19x8). I t encompasses probabilistic costs such as predation risk and expected Ibraging payofrs. as well as nioi-e dircct cncrgctlc expenses

194 P. W A T T S

These are logical ( w m e might sag obvious) prerequisites for a siginoid peak, and a diffuse aLvareness of them pen adcs the literature. Die1 changes in prey availability are commonly cited to explain why seals forage nocturnally (Boulva & McLaren. 1979; Hansen, 1979; Haaker ct d., 1 984). Conversel!,. the absence of signioict peaks have been associated with prey whose availability does not change throughout the day (e.g. Fay. 1982; Stewart & Yocheni. 1989). To ink knowledge. h o \ \ ~ c \ ~ r . all f o u r conditions have not been presented before as a necessary and sufficient combination. I t seems therefore appropriate to discuss them here in greater detail (with the exception of thc first condition, which is triviall) self-evident).

C'O.\/ o f ' i /7? /~W/ . .YiO/!

The cost of pinnipcd locomotion is not trivial. A harbour seal swimming at thc energetically inost efficient speed o f 1.4 m . hec (Williams & Kooynian. 1985) has about twice the metabolic rate o1';i resting animal (Dal i \ . Williams & Kooynian. 1985). Yet these animals travel significant dihtancrs nhen comniuting bet\\ cen Iuntl-oiit and foraging areas (up to 40 kinlday- Bjorge et ( I / . .

199 1 ). f-urtherniore. time spent outside the feeding grounds represents missed foraging oppor- tunities. Such costs could be ;I\ oided by simply remaining ;it the foraging site; the fact that seals do n o i do this (at least in those cases \vhere ;t signioid peak exists) implies soinc other. greater cost associated \vi th con t in~ i~d immersion.

Therc. lias been little investigation of such ;I cost. Although it i s sonietiines assumed that h a r b o u r seals /utu/-o//{ for tlicriiioregulator~ reasons (Felt7 & Fay, 1966; Boulva & McLaren. 1979: King. 198.3: Lavignc & Schniitz. 1990). ;I body of n ,o rk suggests that phocids are normally tliei-m~~iicutr~il wlicn ininicrsect (Ash\vell-Erickson & Elsner. 1980: Lavigne, 1982: Schniitz & LaLipnr. 1984: La~ignc ( i t "/,. l986(1./~. 1990: Innes ct a/.. 1987: Innes & Lavigne, 1991). Most of these studies. hone\er . assume an average daily metabolic rate (ADM R) of about twice the basal rate. :illo~-ing seals to take advantage of heat generated during exercise. Sleeping harbour seals (\vith roughly basal metabolic rates--- Ashwell-Erickson & Elsner. 1980) might not be thernio- neutral 111 \{Liter colder than 15Sl7'C (Watts. Hansen, & Lavigne. 1993). I t is therefore possible t h a t seals irciiti-oitr to a v o i d elevated nietabolic costs \ \Me asleep.

Anothcr immcrson cost that lias received very little attention i s the risk of encountering aquatic pred:iioi-\. Predation 15 probleniatic for pinnipeds around the globe (e.g. Hancock. 1965: Siniff & Bengston. 1977: Conclq. van Aarde & Bester. 1978: Smith ct d., 1981; Brodie & Beck. 198.7: Fa!. Seasc & Merrick. 1990: Frost. Russell & Lo\vi-y. 1992: McConnell t'f o/., 1992). Along the coa5t ot' British Columbia. 'transient' killer whales are ;I conspicuous predator of harbour scals ( B i g c'i ( I / . . 1987: Bairct & Stacey, 1988). There are insufficient data to deterinine reliabl! the impact of killer \\hales o n harbour seal populations, but initial estimates based on observed predation rate5 i n British Columbia waters (Baird. Wkitts & Stacey. 1989) suggest that a n individual c ; t l mag' csperience approximatel) ;I 50-80% chance of being eaten before reaching I-eproductiw age (Watts. 1991 ). These values are a t best inexact (given the paticit! of' a \ ailable data and ;I consequent reliance on simplifying assumptions), but they seri'c to indicate the generlil scale of risk from aquatic predators that harbour seals face. They ma! in fact be conscr\ ative since they ignore the predatory attentions of sharks, another signilicunt wt~i-cc o f pinniped mortality along the Pacific coast (Schef€er & Slipp. 1944: Ainley C'I 01.. 1% I : 'Ti-illmich & Moliren. 198 I : Le Boeuf. Kiedman & Keyes. 1982).

In the case of'tlic harbour seals at Snake Island. i t i s unclear whether the implicated 'cost of iminersion' is ti1oi-c likel! due to predation risk. to thermoregulation while sleeping, or to

I

D l E L H A U L I N G - O U T C Y C L E O F SEALS 195

something not considered here3. Harbour and grey seals sometimes rest in shallow water, even when I?uul-out sites are available (Thompson, D. et al., 1991; McConnell et a/., 1992; pers. obs.). suggesting that thermoregulation may not be an overriding concern. If, instead, harbour seals lw.d-out to avoid aquatic predators, those on extended offshore trips might be expected to forage continuously to minimize their overall time at sea. This appears to be true in some cases (Stewart & Yochem, 1989), but not in others (Bjorge et al., 1991).

Noctllr-nal peuk in prey availabilitj~

Although evidence for its prevalence at Snake Island is circumstantial (Fig. 2; Watts, 1993). nocturnal foraging is consistent with both the nature of the seals' prey and the physiography of the surrounding area. Beyond the increased availability of vertically-migrating prey at night (see Methods), pinnipeds which hunt small numerous prey may also be energetically constrained from diving as deeply as those which hunt larger prey (Costa, 1991). Harbour seals in the Strait of Georgia prey mainly upon small, schooling fish (Olesiuk et al., 1990h), vertical niigrators that approach the surface at night (Beamish, 1966; Harden Jones, 1968; Blaxter, 1985). In areas where prey retreat to deep water during the day, nocturnal foraging appears to be the norm for harbour seals (Boulva & McLaren, 1979; Hansen, 1979; Allen, 1989; Thompson et al.. 1989). More broadly, pinnipeds showing a sigmoid peak generally feed upon vertically-migrating prey (see King, 1983 for reviews). Harkonen (1987) presents a conceptual model relating prey availability to water depth.

Costs of rrnzaiviing at .sea exceed commuting costs

If travel costs exceeded those of remaining at the foraging site, there would be no reason t o lzcrul-out between foraging bouts; consequently, no sigmoid peak would appear. Since loconio- tory costs increase with distance travelled, a sigmoid peak should assert itself only if foraging occurs within some critical radius of the Izazrl-out site. In fact, data from radio-tagging studies suggest that the sigmoid peak may not appear if harbour seals forage more than 10-15 km from their haul-out sites (Thompson et al., 1989; Thompson & Miller, 1990). Instead, seals that feed during extended offshore trips (Thompson et a/., 1989; Stewart & Yochem, 1989; Thompson & Miller, 1990; Harvey & Torok, 1994) tend to haul-out for extended periods (< 48 h) after returning inshore.

It is impossible to draw firm conclusions from these data, due to small sample sizes and the confounding influence of season and gender confounds; the 10-15 km range limit is, however, consistent with data collected from other pinnipeds (e.g. Boyd, Lunn & Barton, 1991). Whether it applies to the Snake Island harbour seals is an open question.

E.vcepti0n.s to the sigmoid perk

There are a number of Iirrul-out sites for which no sigmoid peak has been reported. For the most part, these are either subject to severe constraints on site availability (e.g. Paulbitsky. 1975;

A third cost of immersion, relevant only to phocids living in polar environments. is restricted access to breathing holes. Lack of air imposes a far more immediate cost than predation risk, which would cxplain why ringed seals (P/roc,tr lii.c.pit/tr) haul-out (Smith. 1973; Finley. 1979) even though this puts them at risk from terrestrial predators (Stirling. 1975)

196 P. W A T T S

Sullivan. 1980: Renoufcc (11.. 1981). or they fail to fulfil the four conditions discussed above. For example. seals that conccntrate on benthic prey (not vertical niigrators) forage and Iznul-ou/ with no particular regard for time of day (Stewart & Yochcm. 1989; Bjorge el al., 1991). Daylight foraging can also occur Lvhen the descent of vertically-migrating prey is constrained, in estuaries and shallow bays fo i - example (e.g. Boulva & McLaren, 1979: RoITe & Mate. 1984). Where local topography causes prey a\,ailability to peak during daylight. the usual midday hauling-out cycle can invert (Thompson. P. M. ot ul.. 1991). In other instances, apparent lack of a sigmoid peak may be due to limited sample size (Yochem r t al.. 1987--see Introduction).

The sigmoid peak is only a statistical portrait of g r o ~ i p behaviour, not a rule to which every member must adhere. For this reason. it may be inappropriate to search for it below the level of the colony. The significance of any statistical relationship. however, increases with sample size; it would be interesting to determine what minimum group size is necessary for the persistence of a significant sigmoid peak in harbour seals.

Thanks are due to Mike Ho\ven. 1r;t LeRoi. Andrew Trites. and especially Kate Keogh for assistance in Lhe field: .I. D. McPhail and Jamie Smith for the loan of vital equipment: and David M. Lavigne, Janet Wallace, and t w anonymou~ re\ icivcrs. who criticized early drafts of this manuscript. Jethro Tull maintained a persistenr if subtle inlluencc. Field a o r k \\;is partially funded by a n NSERC operating grant to Carl .I. Walters of the LniLerhiry of British Columbia; the transformation ot'raw data into final dr-aft was funded by an I M M . 4 postdoctoral f'ello\vship to the author.

D I E L H A U L I N G - O U T C Y C L E O F SEALS 197

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D I E L H A U L I N G - O U T C Y C L E O F SEALS I99

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