Population assessments of gentoo penguins (Pygoscelis papua) breeding at an important Antarctic tourist site, Goudier Island, Port Lockroy, Palmer Archipelago, Antarctica

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Population assessments of gentoo penguins (Pygoscelis papua) breeding at an important Antarctic tourist site, Goudier Island, Port Lockroy, Palmer Archipelago, Antarctica P.N. Trathana,*, J. Forcadaa, R. Atkinsonb, R.H. Downiea, J.R. Shearsa a

British Antarctic Survey, High Cross, Madingley Road, Cambridge CB3 0ET, UK Carrick House, Camus-Na-Ha, Fort William PH33 7NN, UK

b

A R T I C L E I N F O

A B S T R A C T

Article history:

Goudier Island is located in the Palmer Archipelago, to the west of the Antarctic Peninsula;

Received 3 March 2008

it is one of the most frequently visited tourist sites in Antarctica. A number of gentoo pen-

Received in revised form

guin (Pygoscelis papua) breeding colonies are located on the island and these have been the

2 September 2008

focus of one of the longest running experiments to examine the impacts of tourist numbers

Accepted 5 September 2008

upon penguin breeding performance anywhere in the Antarctic. In this paper we describe

Available online 21 October 2008

the population trends and breeding productivity (chicks per nest) of the 10 colonies on Goudier Island, all of which have now been monitored for 12 consecutive years beginning in the

Keywords:

1996/1997 breeding season. Our results demonstrate that all colonies show considerable

Gentoo penguin

inter-annual variability for both the number of breeding pairs and breeding productivity.

Long-term monitoring

Of the six visited colonies, two showed an important and significant statistical decline in

Goudier Island

the number of breeding pairs. One of these declining colonies is used to determine the

Port lockroy

breeding chronology dates for all other colonies, an important part of the monitoring pro-

Tourism

cedure used to assess breeding success. Our results suggest that in the future, it would be

Antarctica

useful to control for this additional disturbance. Our results further suggest that understanding all of the many subtle influences that impact upon gentoo penguin breeding numbers is complex and that some factors may never be completely identified. Crown Copyright  2008 Published by Elsevier Ltd. All rights reserved.

1.

Introduction

In recent years, the number of tourists visiting Antarctica has risen markedly, with tourist numbers having increased from 7413 in 1996/1997 to 29,530 in 2006/2007 (IAATO, 2007). For example, at Goudier Island (6449 0 S, 6329 0 W), to the west of the Antarctic Peninsula (Fig. 1), tourist numbers have risen steadily during this same time period, having increased from 4292 to 16,004. Indeed, Goudier Island is now one of the most visited tourist sites in Antarctica. Goudier Island is situated within Port Lockroy off Wienke Island in the Palmer Archipelago; tourists visit the island to see the restored British Antarc-

tic Survey (BAS) base, now managed by the UK Antarctic Heritage Trust (UKAHT), and a number of breeding colonies of gentoo penguins (Pygoscelis papua). As the numbers of tourists visiting Antarctica have increased, concerns have been expressed about the potential disturbance caused by visitors. Disturbance may be caused by a number of activities, including by visitors approaching too close to penguin colonies (Culik and Wilson, 1995). As such, a number of studies have now measured a variety of factors in order to look for evidence of tourist impacts on penguin behaviour. These studies include observations on heartrate (Nimon et al., 1995), on stress hormones (Walker et al.,

* Corresponding author: Tel.: +44 1223 221602; fax: +44 1223 32616. E-mail address: [email protected] (P.N. Trathan). 0006-3207/$ - see front matter Crown Copyright  2008 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.biocon.2008.09.006

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Fig. 1 – Maps showing: (a) the location of the individual gentoo penguin colonies on Goudier Island; (b) the location of the Palmer Archipelago and (c) the location of Goudier Island.

2005; Ellenberg et al., 2007), on penguin behaviour whilst travelling overland between the sea and the colony (Martin et al., 2004; Burger and Gochfeld, 2007), on breeding distribution (Woehler et al., 1994), and on breeding performance, fledging success and population trends (Giese, 1996; Fraser and Patterson, 1997; Cobley and Shears, 1999; McClung et al., 2004; Holmes et al., 2006; Carlini et al., 2007). Despite the diversity of species, experimental approaches and observations, there is still little general consensus in the conclusions drawn from these studies. As a result, there is still considerable uncertainty about the magnitude and significance of tourist impacts upon breeding penguins. The lack of general consensus is potentially because of the wide variety of species studied, the different locations used, and the assorted levels and type of human activity to which penguins were exposed. Consequently, if any general conclusion can be drawn from the literature to date, it is that penguin responses to human activity are species-specific, activity-specific and even location-specific (e.g. see Holmes et al., 2005, 2006; Holmes, 2007). Consequently, many studies have advocated a precautionary approach with regard to the management and the regulation of tourist numbers and tourist proximity

to colonies (Wilson et al., 1991; Simeone and Schlatter, 1998; Simeone and Bernal, 2000; Holmes et al., 2005). Many of the studies carried out so far have been of a relatively short duration (typically one to three years in duration). As a consequence, inter-annual variability, such as caused by unusual weather conditions (e.g. Cobley and Shears, 1999), or other similar confounding factors, can potentially dominate results. Long-term studies using consistent methodologies are therefore especially important when it comes to elucidating potential tourist impacts on penguins. Such studies should allow for short-term impacts of the environment to be analysed separately from tourist pressures. Similarly, studies should encompass a range of colony sizes and should avoid small colonies that are most susceptible to edge effects (Jackson et al., 2005) and possible disturbance by raptorial seabirds such as skuas and giant petrels. Stochastic variability is also most likely to be important in small colonies, where individual movements and demography will be important. The study at Goudier Island has now been established for 12 years and is one of the longest running tourist monitoring programmes in Antarctica. Historical data from Goudier

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Island show that the colonies were first established in 1985 and rapidly increased in size until the late 1990s. The neighbouring colony at Alice Creek, Jougla Point on Wiencke Island has also increased in size, although this expansion has been at a slower rate (Cobley and Shears, 1999); for at least 20 years, this colony has also been regularly visited by tourists. The study of tourist disturbance on the breeding performance of gentoo penguins at Goudier Island was established in 1996/1997 (Cobley and Shears, 1999; Cobley et al., 2000). The experimental protocol divided the island (and the colonies) into a control area where tourists were not permitted to enter, and a visited area where tourists may walk around the penguin colonies closely supervised by their tour leaders. All tourist visits follow site visitor guidelines agreed by the Antarctic Treaty Consultative Parties (ATCM, 2004). During the early years of the study, no tourist impacts were evident when comparisons were made between control colonies and visited colonies (Cobley and Shears, 1999). At this time, Cobley and Shears (1999) reported that visited colonies were seen by 35–55 tourists every 1–2 days, and that no differences were evident between the two groups for a number of metrics, including the proportion of birds that laid, hatching success, and the proportion of single-chick broods. Of note though was the fact that these early studies recorded a higher proportion of single-egg clutches in the visited colonies, although this was attributed to colony location and unusually high snow accumulation. Today the same colonies are visited more frequently with up to 60 tourists allowed on the island at any one time (exclusive of expedition guides and leaders, with 1 guide to every 20 visitors), with no more than 350 visitors per day (ATCM, 2004; UKAHT, 2006). The study at Goudier Island is now 12 years in duration and thus provides a valuable long-term dataset that documents the potential impacts of tourists on gentoo penguins over several seasons. Indeed, it is only with such long-term datasets using consistent methodologies that the impacts of controlled and regulated tourism may be detected. Thus, the principal objectives for this study were to describe, with a long time series, the population trajectories of gentoo penguins breeding at a frequently visited tourist site and to determine whether impacts could be detected that could be ascribed to tourist impacts, as opposed to environmental forcing (Forcada et al., 2006; Trathan et al., 2006).

2.

Methods

The study was carried out at Goudier Island, Port Lockroy (Fig. 1) between 1996/1997 and 2007/2008. Various aspects of the breeding biology of gentoo penguins at Goudier Island have been recorded since 1996/1997. These metrics have been collected using the Standard Methods () and protocols detailed in the Convention for the Conservation of Antarctic Marine Living Resources (CCAMLR) Ecosystem Monitoring Programme (CEMP). Breeding chronology (Method A9) was determined on an annual basis; the ‘‘Mast’’ colony was used to determine the breeding chronology for the whole island. Method A9 requires that individual nests and the associated bird(s) are marked to facilitate identification, this means that field staff

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must enter the colony, potentially causing a greater level of disturbance than simple observing or counting from outside the colony perimeter. The main count to determine annual trends in breeding population size (Method A3A) was made one week after 95% of pairs had laid their clutch. To estimate breeding success (Method A6), a count of the number of nests with chicks was made when hatching had ended. Method A3A and Method A6 do not require the field staff to enter the colony. The visited colonies on Goudier Island vary in size from just a few nests to over 175 and each receives differing levels of visitation. No formal records of tourist numbers visiting each colony are maintained, though the most visited are the ‘‘Boat’’, ‘‘Base’’, and ‘‘Anemometer’’ colonies (Fig. 1). The ‘‘Mast’’ and ‘‘Screen’’ colonies receive relatively fewer visitors.

2.1. Linear regression models and mixed-effects models for birds on Goudier Island Our colony data were analysed using linear regression models, robust-resistant regression models (Yohai et al., 1991) or mixed-effects models (Pinheiro and Bates, 2000) implemented in R (R Development Core Team, 2003). Despite low counts in some colonies, there was no evidence to suggest that counts were not approximately normally distributed. We used a variety of models to examine both the control colonies (not visited by tourists) and the visited colonies (visited by tourists). Models were compared using AICc (using the low sample version of the AIC criterion). The lowest AICc value indicates the best model; models with differences of less than 2 AICc units indicate similar abilities to explain the data.

2.2.

Counts at Jougla Point

Opportunistic counts of gentoo penguin nests and chicks have been made at Jougla Point on Wiencke Island since 1996/1997. Jougla Point is some 150 m south from Goudier Island and so forms a useful comparison site. Tourists visit the penguins on Wiencke Island, though no accurate records of visitor numbers are maintained.

3.

Results

The habitat available to penguins breeding on Goudier Island has not changed during the course of this study; thus, though snowfall in some years has affected breeding numbers (Cobley and Shears, 1999; Cobley et al., 2000), patterns of snowfall have not permanently altered the potential breeding habitat. The gentoo penguin colonies at Goudier Island all showed considerable inter-annual variation in the numbers of pairs breeding and in the numbers of eggs produced (Fig. 2). There were major perturbations in both the 1998/1999 season and particularly in the 2001/2002 season; the causes of these impacts are unknown, though they are most likely due to environmental effects such as heavy snowfall. After the 2001/ 2002 season, the visited colonies did not recover to the same number of breeding pairs. The control colonies (not visited by tourists) generally showed similar levels of variability to the visited colonies (visited by tourists), but these colonies did

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Fig. 2 – Gentoo penguin breeding pairs (occupied nests) and productivity (chicks) at Goudier Island (left hand axis). j: Breeding pairs in visited colonies; h: breeding pairs in control colonies; d: productivity in visited colonies; s: productivity in control colonies. Gentoo penguin breeding pairs (occupied nests) at Jougla Point (right hand axis) and : breeding pairs. Years represent the year of fledging.

return to a similar number of breeding pairs after the perturbations of the 2001/2002 season.

3.1.

Tourist numbers at Goudier Island

Total tourist numbers visiting Goudier Island have increased considerably since the start of the study in 1996/1997 (Fig. 3). The date of the first tourist vessel visit varies each year, depending upon vessel cruise itineraries; no consistent patterns were evident between the timing of the first visit and gentoo penguin population trend or breeding productivity.

3.2. Linear regression models and mixed-effects models for birds on Goudier Island 3.2.1.

1st analysis

The response y is the number of occupied nests with an independent trend model for each type of colony (visited or con-

trol). Simple linear regression models and robust-resistant regression models (to account for outliers (Yohai et al., 1991)) were used to show the trends over time. Predictors are: year (Y); type of colony (T), which is a factor indicating whether a colony is a control colony or not; and the interaction of type of colony and year (Y * T); e is an error term; + is an additive effect; * is an interaction effect and a an intercept. Results for the linear regression models are shown in Table 1; the best model was model 4. Trends for the linear regression models and the robust regression models are shown in Fig. 4.

3.2.2.

2nd analysis

The response yij is the observed productivity, defined as the number of chicks produced divided by the number of nests with eggs, for observation (year, colony, type) j in colony, year or type i. Simple linear regression models and mixed-effect regression models were used to show the trends over time.

Fig. 3 – Number of tourists visiting Goudier Island in each year of the study. Years represent the year that chicks in the study fledged. Note: in 2005/2006 some tour ships visited the island before the station was occupied.

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Table 1 – Results from simple linear regression models to predict the number of occupied nests (1st analysis) Bn parameters

Model 1. y = a + e 2. y = a + B1Y + e 3. y = a + B1Y + B2T + e

P-value

– B1 = 6.5 B1 = 6.5 B2 = 153.6 B1 = 6.5 B2 = 20087.6 B3 = 9.95

4. y = a + B1Y + B2T + B3Y * T + e

– 0.540 0.161 <0.001 0.120 0.020 0.021

R2

AICc

– 0.02 0.81

318.8 320.3 280.3

0.87

275.8

The response y is the number of occupied nests. Predictors are: year (Y); type of colony (T), which is a factor indicating whether a colony is a control colony or not; and the interaction of type of colony and year (Y * T). e is an error term, Bi are main effects (regression slopes) and a an intercept.

900

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VISITED COLONIES slope: -15.43; P < 0.001 slope: -16.42; P = 0.04

850

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800

CONTROL COLONIES slope: 3.14; P = 0.70 slope: 3.48; P = 0.35

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Fig. 4 – Results from linear and robust-resistant regression models to predict the number of occupied nests with trend lines for each colony type (1st analysis; model 4). Continuous lines are robust-resistant regression models, with their 95% confidence limits shown by dotted lines, and dashed lines are the linear regression slopes. Years represent the year of fledging.

Predictors are: year (Y); type of colony (T), which is a factor indicating whether a colony is a control colony or not; and colony (C). b are fixed (population) effects and bi are random deviations of the population mean for colony (biC), year i (biY) or type i (biT); eij is an error term representing the deviation in productivity for year j on colony i, or colony j on year i, ^2b is depending on the model. In random-effects models, r 2 ^ , equivathe ‘‘between-colony, year or type’’ variability, and r lent to eij, is the ‘‘within-colony or year’’ variability. Results from the simple linear and linear mixed-effects ^, with models to predict the mean expected productivity p residual standard error r ^2 are shown in Table 2. Model 1 is a ^ ð^ null model where b is the population mean p r2 Þ, and model 2 ^ i ð^ r Þ. P-values according to an 2 is a fixed-effects model, bi ¼ p F-statistic and AICc are the small sample version of the Akaike Information Criterion. For model 2, the order of colonies under productivity and visited were ‘‘Anemometer Tower’’, ‘‘Base’’, ‘‘Boatshed’’, ‘‘Mast’’, ‘‘Nissen’’ and ‘‘Screen’’, and under control, ‘‘Control 1 to 4’’ (from top to bottom and

from left to right in Table 2). For model 3, there were no colony effects and years are from 1996/1997 to 2007/2008 (from top to bottom and from left to right in Table 2). The best model was model 5 (Table 2), which suggests that the highest variability in productivity was between colonies. Model 8 (Table 2), the next best model, shows that the effects by type of colony were marginal, with a difference of approximately less than 9% productivity. The fit of model 8 was less good than that of model 5, based on the mean estimated productivity by colony, most of the difference by type of colony was likely driven by the extremely low productivity at one visited colony, that is the ‘‘Nissen’’ colony.

3.2.3.

3rd analysis

The response yi is the number of occupied nests at each colony with an independent trend model for each colony. Linear regression models and robust-resistant regression models were used to show the trends over time. Almost all the regression trends were negative for the visited colonies, although

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^ (2nd Table 2 – Results from simple linear and linear mixed-effects models to predict the mean expected productivity p analysis), with residual standard error r ^2 Model

P-value for b

Visited 1. yij = b + eij

<0.001

15.1

2. yij = biC + eij

<0.001

55.4

3. yij = biY + eij

<0.001

30.7

4. yij = biT + eij

<0.001

13.7

5. yij = b + biC + eij

<0.001

75.4

6. yij = b + biY + eij

<0.001

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7. yij = b + biT + eij

<0.001

15.6

8. yij = bjT + biC + eij

<0.001

71.5

^2 r

^Þ Mean productivity (p

AICc

Control b = 0.86 (0.82–0.90)

bi = 0.94 (0.85–1.02) 0.91 (0.83–1.00) 0.90 (0.82–0.99) 0.92 (0.83–1.01) 0.38 (0.29–0.47) 0.91 (0.82–1.00) bi = 0.91 (0.77–1.04) 0.92 (0.79–1.06) 0.79 (0.66–0.93) 0.85 (0.72–0.99) bv = 0.83 (0.78–0.88)

^2b r

bi = 0.93 0.91 0.92 0.91

bi = 0.84 0.77 1.00 0.89

(0.70–0.97) (0.64–0.91) (0.86–1.13) (0.75–1.02)

(0.85–1.02) (0.82–1.00) (0.83–1.01) (0.83–1.01)

bi = 0.95 (0.81–1.08) 0.85 (0.72–0.99) 0.84 (0.70–0.97) 0.76 (0.62–0.89) bc = 0.92 (0.86–0.98)

b = 0.86 (0.76–0.97) b + bc = 0.93, 0.91, 0.91,0.91 b + bv = 0.92, 0.91, 0.90, 0.91, 0.41, 0.91 b = 0.86 (0.82–0.90) b = 0.87 (0.78–0.96) b + bc = 0.91 b + bv = 0.83 bv = 0.83 (0.66–0.99) bc = 0.92 (0.72–1.12) bc + bc = 0.99, 0.96, 0.97,0.97 bv + bv = 0.90, 0.87, 0.87, 0.88, 0.38, 0.88

0.22 (0.19–0.25) 0.15 (0.13–0.18)

– –

0.22 (0.19–0.25)

–

0.22 (0.19–0.25) 0.15 (0.13–0.17) 0.22 (0.19–0.25) 0.22 (0.19–0.24) 0.15 (0.13–0.17)

– 0.17 (0.10–0.27) 0.03 (0.00–0.88) 0.06 (0.01–0.32) 0.17 (0.10–0.28)

The response yij is the observed productivity as the number of chicks produced divided by the number of nests with eggs, for observation (year, colony, type) j in colony, year or type i. Predictors are: year (Y); type of colony (T), which is a factor indicating whether a colony is a control colony or not; and colony (C). b are fixed (population) effects and bi are random deviations of the population mean for colony (biC), year i (biY) or type i (biT); eij is an error term representing the deviation in productivity for year j on colony i, or colony j on year i, depending on the model.

not all were significant. Trends were significant from the linear models for colonies ‘‘Anemometer Tower’’, ‘‘Mast’’ and ‘‘Nissen’’; trends from the robust models were significant for colonies ‘‘Anemometer Tower’’ and ‘‘Nissen’’. These results are shown in Fig. 5a. The ‘‘Control 3’’ colony showed a significant increasing slope (robust regression) (Fig. 5b); the corresponding linear slope was non-significant at the 5% level.

3.3.

Counts at Jougla Point

The first count of gentoo penguin nests at Jougla Point on Wiencke Island was made in 1996/1997 when 1349 pairs were recorded. The most recent count was made in 2007/2008 when 1276 pairs, some 5.4% fewer, were recorded. This compares with a reduction of some 17.9% in the visited colonies on Goudier Island over the same time period. The highest counts at Jougla Point were in 2000/2001, the same year that numbers on Goudier Island also peaked (Fig. 2).

4.

Discussion

Environmental processes influence gentoo penguin breeding biology over a range of spatial scales. These can vary from very large ocean basin scales to very local site-specific scales (scales of hundreds of metres). At the larger scale, Goudier Island is one of the more southerly breeding localities for gentoo penguins which are now thought to be extending their range southward in response to regional climate change and environmental forcing factors (Fraser et al.,

1992; Trathan et al., 1996; Forcada et al., 2006; Ducklow et al., 2007; Hinke et al., 2007). At this broad regional scale there are evident differences between sites and years that potentially reflect the complex interactions between penguins, their prey and the environment (Forcada et al., 2006). For example, gentoo penguin populations at nearby Anvers Island have continued to increase in recent years (Ducklow et al., 2007), while populations at King George Island in the South Shetland Islands have remained stable (Hinke et al., 2007), and populations at Signy Island in the South Orkney Islands have increased, reaching a maximum in 2000/2001, before stabilizing (or possibly decreasing a little) (BAS unpublished data). In line with these changes, the colonies at Jougla Point on Wiencke Island increased until 2000/2001 and then decreased thereafter; however, the counts there have been sporadic and are not undertaken in every year, principally due to logistic constraints (there is no boat available to field staff on Goudier Island). Thus, broad regional patterns of gentoo penguin population change are complex and not yet fully understood. At much smaller scales the environment can have equally important impacts. For example, the influence of late snow in very localized areas (even on a small island just 175 · 125 m in size) is known to impact negatively upon gentoo penguin breeding success (Cobley and Shears, 1999; Cobley et al., 2000). Similarly, at this small scale, gentoo penguins are renowned for the mobility of their nesting sites despite a high level of phylopatry, particularly when located on vegetated areas (Bost and Jouventin, 1990). The factors governing the

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Fig. 5a – Results from linear and robust-resistant regression models to predict the number of occupied nests at the visited colonies, with trend lines for each colony (3rd analysis). Continuous lines are robust-resistant regression models, with their 95% confidence limits shown by dotted lines, and dashed lines are the linear regression slopes. Years represent the year of fledging.

exact locations of colonies at the small scale are as yet unknown, but it is highly plausible that the movement of individuals does occur between sites on Goudier Island and between Goudier Island and Wiencke Island. With such different and complex environmental factors impacting upon the breeding biology of gentoo penguins, the environment potentially confounds any analysis of tourist impacts upon any specific colony. Consequently, having a number of colonies at one site, some of which are visited and some of which are not, is key to interpreting penguin population trajectories in response to potential tourist disturbance. Equally, having a long-term study that includes 12 years of data allows short-term environmental impacts to be distinguished from longer-term trends. The combined analysis to predict the number of occupied nests in the colonies on Goudier Island (1st analysis) indicated that there was a significant difference in the population trajectories of the control colonies and the visited colonies. The best model (model 4 in Table 1; Fig. 4) explained 87% of the variance, indicating a good level of fit to the data. However, the combined analysis to predict the observed productivity (2nd analysis) indicated that the highest variability in productivity was between colonies (model 5 in Table 2) and that the effects by type of colony were marginal (model 8 in

Table 2). Taken together, these results suggest that there may be some impacts by tourists on the visited colonies, primarily upon the numbers breeding, rather than upon the productivity of those that breed. The visited colonies show a significant decline in nest numbers, whereas the control colonies show stable nest numbers, or in one case, even a slight increase. A plausible explanation for our result is that tourists are affecting the visited colonies and that this leads to a reduced number of birds nesting at these sites. Such impacts may actually be quite considerable, if in general most colonies (as characterized by gentoo colonies elsewhere near to the Antarctic Peninsula) are actually growing in response to altered ecosystem properties resulting from climate change (Forcada et al., 2006). However, a separate, equally plausible scenario is that tourists are affecting the visited colonies but causing a movement of breeding birds away from them to the control colonies so that these are growing in size. Under this latter scenario, rates of decline may be lower than under the former scenario. Nevertheless, under both scenarios, once having chosen a nest site, birds were not further influenced by visitors, as there was no evident significant impact upon breeding performance. If movement between colonies is happening at Goudier Island (or between Goudier Island and Wiencke Island), it

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Fig. 5b – Results from linear and robust-resistant regression models to predict the number of occupied nests at the control colonies, with trend lines for each colony (3rd analysis). Continuous lines are robust-resistant regression models, with their 95% confidence limits shown by dotted lines, and dashed lines are the linear regression slopes. Years represent the year of fledging.

may be that some birds are more likely to move than others. This would be the case, for example, if some birds remain averse to tourists and some habituated. Both behaviours have been identified as important factors for gentoo penguins breeding on Macquarie Island in areas disturbed by humans (Holmes et al., 2006). Thus, on Goudier Island, there may be long-term breeders within the visited colonies that do not consider tourist activity to be a threat and these birds therefore continue to breed within the visited area. These birds may have bred at this site since before controlled tourism began, or prior to some lower visitation threshold after tourists began visiting the island, and have subsequently habituated. In contrast, younger prospecting breeders may assess current levels of visitation as a threat and therefore not recruit into the visited colonies and so move to unvisited areas (Woehler et al., 1994). Such a scenario would not be identified in our measure of breeding productivity but would be evident in our measure of breeding population, particularly as habituated breeders age and become senescent and reduced numbers of prospecting birds recruit to the visited colonies. Though plausible, such hypotheses are undoubtedly simplistic and overlay considerable complexity as revealed by the 3rd analysis; this analysis provides an independent trend model for each colony. At the visited colonies the regression models show mostly negative trends for the number of occupied nests (Fig. 5a); these trends were significant (at the 5% le-

vel) for the ‘‘Anemometer Tower’’ and ‘‘Nissen’’ colonies (linear and robust regression models) and for the ‘‘Mast’’ colony (robust regression model only). At most of the control colonies no trends were apparent in the number of occupied nests (Fig. 5b); however, there was a significant and positive trend for the number of occupied nests at the ‘‘Control 3’’ colony (robust regression model only). These analyses indicate that if there are impacts from tourists, they are not equal across all colonies. Indeed, three of the six visited colonies were not impacted and showed no significant trends with stable nest numbers and stable productivity. The two colonies that showed the most marked declines were the ‘‘Anemometer Tower’’ and the ‘‘Mast’’ colonies. The ‘‘Anemometer Tower’’ colony is one of the more frequently visited colonies on the island, but not the most visited colony. In contrast, the ‘‘Mast’’ colony is one of the less frequently visited colonies though it is the ‘chronology colony’ used to assess the phenology for the whole island population. That this colony showed a marked decline suggests that the impacts of chronology observation maybe at least as great as those of visiting tourists, and possibly even greater (cf Fraser and Patterson, 1997). Thus, the declines observed in these two colonies highlight just how local some factors may be. Furthermore, they illustrate that understanding all of the many subtle influences that impact upon breeding numbers is complex and that some factors may never be completely identified or controlled for.

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5.

Conclusions and recommendations (1) The gentoo penguin breeding colonies at Goudier Island show considerable inter-annual variability in both the number of nesting birds and in the productivity of chicks. Similar results have been shown previously across the breeding range of gentoo penguins (e.g. Holmes et al., 2006; Pu¨tz et al., 2001). (2) Some colonies visited by tourists show significant declines in the number of nests, but no similar trend in breeding productivity. Such declines may be because juvenile birds recruit to unvisited colonies, while established breeders continue to breed in the visited sites (Holmes et al., 2006). The control colonies show no such declines. (3) Detailed analysis of individual colonies revealed that scientific observer effects could be important and may be impacting upon the colony used to assess the breeding chronology of the gentoo penguins breeding on the island. (4) To test whether scientific observer effects are important, it is recommended that the ‘‘Screen’’ colony is now used to assess breeding chronology. This would remove any potential observer effect from the ‘‘Mast’’ colony. Historically, the ‘‘Screen’’ colony has received very few visits by tourists and it is relatively stable in size (Fig. 5a). (5) Environmental covariate data should be recorded at Goudier Island, including days of snow cover at each colony, date of breakout of sea-ice, location of raptorial seabird nests, etc. This will allow for increased sophistication in future analyses, allowing environmental effects to be better addressed (Holmes et al., 2006).

Acknowledgements We thank all of the staff of the British Antarctic Survey and the Antarctic Heritage Trust who helped with data collection at Goudier Island.

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