ORIGINAL ARTICLE

Effects of anthropogenic food resources on yellow-leggedgull colony size on Mediterranean islands

Celine Duhem Æ Philip Roche Æ Eric Vidal ÆThierry Tatoni

Received: 28 December 2006 / Accepted: 3 July 2007 / Published online: 21 August 2007

� The Society of Population Ecology and Springer 2007

Abstract Yellow-legged gull Larus michahellis popula-

tions have been studied on three archipelagos consisting of

20 islands distributed along 80 km of the French Medi-

terranean coastline. Population changes were analyzed

between 1920 and 2006. In the first decades following their

settlement on these islands, the yellow-legged gull popu-

lations showed a continuous exponential growth in the

three archipelagos, in agreement with an annual geometric

growth rate k above 1. The population growth ceased to fit

this model during the 1980s for the older colonies (Riou

and Hyeres Islands archipelagos). Thus, we focused on

population changes occurring during the period 1982–

2000, a pivotal period for which we have both precise

census and anthropogenic food resource data, in order to

determine environmental factors influencing these popula-

tion changes using multiple linear regression models. An

average annual growth rate of colony size was 1.02 for the

last two decades. The changes in landfill availability, the

gull density in 1982, and the nesting area in 1982 explained

84.4% of variation in colony size changes between 1982

and 2000. The yellow-legged gull changes on the islands in

the last two decades increased as availability in anthropo-

genic food resources increased near the colony (positive

DK). As a consequence, given no reduction in landfill

activity or in accessibility for gulls, we expect this region

to sustain continuous species expansion in the future.

Keywords History of colonization � Landfill �Mediterranean rocky islands � Urbanization

Introduction

The factors determining population size changes are of

central interest for both the conservation of threatened

species and the population regulation of species whose

high number of individuals sometimes results in negative

interactions with biodiversity and/or human interests. In the

case of colonial seabirds, both cases can be found. In

particular, large gull species are extremely similar in terms

of ecology and behavior, and thus their population sizes are

the main factors determining their classification as threa-

tened or not. Several large gull species are opportunistic

feeders and are able to forage on anthropogenic food

resources, such as trawling discards or domestic refuse on

landfills (e.g., Mudge and Ferns 1982; Chudzik et al. 1994;

Pierotti and Annett 1990, 1991). For these species, the

presence, abundance, and nature of anthropogenic food

resources often determine the choice of nesting sites

(Scarton and Valle 1996); the spatial distribution of gulls

during both the breeding season and winter (Fasola and

Canova 1992; Sol et al. 1995); their diet (Belant et al.

1993; Bosch et al. 1994; Oro et al. 1995); and their

reproductive parameters (Bukacınska et al. 1996; Oro et al.

2004). Thus, a high availability of anthropogenic food

resources often leads to an increase in recruitment rates,

notably because of a probable decrease in winter mortality

of the youngest gulls, as well as significantly higher

reproductive success (Pons and Migot 1995; Brousseau

C. Duhem (&) � P. Roche � E. Vidal � T. Tatoni

Institut Mediterraneen d’Ecologie et de Paleoecologie

(IMEP-CNRS-UMR 6116), Universite Paul Cezanne

(Aix-Marseille III), Batiment Villemin,

Domaine du Petit Arbois, Avenue Philibert,

BP 80, 13545 Aix-en-Provence, Cedex 04, France

e-mail: celine.duhem@univ-cezanne.fr

P. Roche

CEMAGREF, Unite EMAX, 3275 Route de Cezanne,

CS 40061, 13182 Aix-en-Provence, Cedex 5, France

123

Popul Ecol (2008) 50:91–100

DOI 10.1007/s10144-007-0059-z

et al. 1996). In turn, a decrease in anthropogenic food

availability is correlated with an increase in the probability

of permanent emigration (Oro et al. 2004).

During the past few decades, several large gull species

have undergone a large increase in abundance, especially

in Europe (e.g., Spaans and Blokpoel 1991) and North

America (e.g., Blokpoel and Spaans 1991). Whereas cer-

tain studies have addressed the effects of food availability

and protection status on population size changes in threa-

tened gull species such as Audouin’s gull, Larus audouinii,

(Martinez-Abrain et al. 2003; Oro et al. 2004), no study

examines this topic for large, nonthreatened gull species

currently undergoing population expansion.

Yellow-legged gull, Larus michahellis, populations have

markedly increased in population size over the past

50 years (e.g., Thibault et al. 1996), especially along the

northern shore of the Mediterranean Sea, where 120,000

nesting pairs have been recorded (Perennou et al. 1996).

These populations are still increasing (BirdLife Interna-

tional 2000) and are considered as problematic given their

negative interactions with human activities and other wild

species. The latter include changes in island vegetation and

terrestrial insect assemblages (Vidal et al. 1998; Orgeas

et al. 2003). Negative effects on waterbirds have also been

reported but are still under debate (Oro and Martinez-

Abrain 2007).

Previous studies have shown that this species feeds on

both trawling discards and landfills (Bosch et al. 1994; Sol

et al. 1995; Gonzalez-Solis 2003). In contrast to trawling

activities, landfills provide highly predictable food

resources because they are daily and locally abundant

throughout the year (e.g., Burger and Gochfeld 1983;

Horton et al. 1983). This particularity may influence the

distribution and population size changes for species that

depend on landfills as their primary food resource, such as

the yellow-legged gull along the French Mediterranean

coast (Duhem et al. 2003a, b; 2005).

Generally, the three suspected determinants for the

strong population increase of several large gull species

throughout the Palearctic during the past few decades were

the legal protection of the species, the protection of the

potential breeding sites, and the increasing availability of

artificial food resources (i.e., refuse in landfills or trawling

discards; e.g., Spaans and Blokpoel 1991). The heteroge-

neous spatial distribution of anthropogenic foraging

habitats (landfills) on the mainland and the availability of

potential breeding sites (coastal islands) along the French

Mediterranean coast, in addition to their differential degree

of protection status, constitute a good model to determine

the main factors influencing yellow-legged gull population

size changes. Thus, we tested the hypothesis that resource

availability, especially changes in landfill accessibility and

domestic refuse abundance, would be the most important

explanatory factor of population size change for the yel-

low-legged gull compared with the topography and

protection status of breeding sites. In this study, we ana-

lyzed the contribution of these variables to changes in

population size observed between 1982 and 2000 on 20

island colonies.

Materials and methods

Study sites

We carried out this study on 20 islands distributed pri-

marily among three archipelagos and along 80 km of the

French Mediterranean coastline (Fig. 1). The western

islands are limestone with scarce scrublands, providing

N

S

EOFRANCE

Marseille

Mediterranean Sea

1-10

11

12

13-20

Colony size in 2000:

λ :1.01-1.05 λ =1 λ<1λ : 1.06-1.19

1

2

3

Frioul archipelago

λ=1.12

0 2km

13

14

15

16

17

18

19

20

Hyères Islands archipelago

λ=0.99

0 10 20km

> 6000 pairs

1000-2500 pairs

100-1000 pairs

10-100 pairs

1-10 pairs

0 2km

4 5

6

7

8

9

10

Riou archipelago

λ=1.02

ToulonHyères

Fig. 1 Map of the study area (southeast France) showing the location

of the 20 islands censused (numbered 1–20, see ‘‘Appendix’’ for name

and islands details) and landfill locations. The changes in colony size

and density between 1982 and 2000 are indicated on each site studied.

Colony size is shown using circles, which vary in size according to

the number of yellow-legged gull pairs censused in 2000, and

variation in grayscale indicates the changes of the colony size (pointsk < 1, white k = 1, grey 1.01 < k < 1.06, dark k � 1.07).

Landfill in use before 1982 Landfill in use after 1982 Coastal

town > 50,000 inhabitants

92 Popul Ecol (2008) 50:91–100

123

suitable nesting sites over roughly the total island area. In

contrast, the eastern islands are granitic with dense vege-

tation, and nesting sites are generally restricted to the

coastal belt and any other poorly vegetated areas. Due to

these differences, total island area does not necessarily

represent nesting area. Thus, to determine the nesting area

(ha), we delineated nesting areas on aerial photographs

using ArcGis 8.0 and field-collected nesting data in 1982

and 2000.

The yellow-legged gull is known to move up to 40 km

from its colony to forage (Witt et al. 1981; Oro et al. 1995).

Within this optimal foraging range, we identified ten large

landfills that were all actively used as foraging sites by

yellow-legged gulls (ca. a minimum of 7,000 gulls per day

and per site; Duhem 2004). These landfills all handle more

than 70,000 tons per year of the same refuse type (i.e.,

mainly domestic waste; ADEME 2000), and they are all

managed in the same way: refuse is covered every evening,

and no pest control occurs. Landfills occur mainly in the

western part of the study area, near Marseilles city, which

is responsible for a strong landfill accessibility gradient,

decreasing from west to east (Fig. 1).

Colony size and population changes

Colony sizes of breeding yellow-legged gulls have been

available since the 1920s for the three archipelagos (Heim

de Balzac 1923; Launay 1983). Using these data, we

assessed the population changes for each archipelago since

1920 by estimating k, the mean annual geometric growth

rate of a population. The growth rate (k) was estimated by a

generalized linear model (GLM) regression analysis of the

number of breeding pairs with time. The exponent of the

regression slope and its 95% confidence intervals (CI) cor-

responded to the population growth rate k and its CI. This

method is suitable because it is robust to both stochastic

environments and census errors and allows for unequal time

census intervals (Oro and Martinez-Abrain 2007).

Since 1982, censuses of yellow-legged gull colonies

have been based on two complementary techniques tradi-

tionally used for seabird populations (Bibby et al. 1992;

Komdeur et al. 1992), thus providing more precise data and

allowing comparison among them (the 1989 census was

excluded because it was a global assessment of colony size

at the archipelago level; Vidal et al. 2004). For accessible

colonies, a marker was deposited in each individually

counted nest. For inaccessible sites, this approach was

replaced by a remote census from a topographic peak or

from a boat, using binoculars. Visibly incubating birds, as

well as individuals stationed in pairs, were counted (Bibby

et al. 1992). In order to study the determinants of population

change, we focused on a pivotal period for the population

growth patterns in our area, i.e., 1982–2000. Between these

two dates, population changes ceased to fit the exponential

growth model, whereas new colonies in the Frioul archi-

pelago islands, settled since 1977 (Launay 1983), showed a

marked increase in size. During this pivotal period, two

precise censuses were performed on 20 rocky islands in

1982 (Vidal 1982; Launay 1983) and in 2000 (Duhem et al.

2008). In addition, we also have precise estimates of landfill

food resource availability for these years (Duhem 2004).

Between these 2 years, we calculated k for 20 rocky

islands with permanent colonies of yellow-legged gulls

using the formula proposed by Migot and Linard (1984)

and Bosch et al. (2000), as the regression method is not

meaningful using two points only.

k ¼ ðNt=N0Þ1=t ð1Þ

t is the number of years between two subsequent censuses,

and N is the number of breeding pairs in 1982 (N0) and

2000 (Nt). In order to obtain standard error estimates for

this parameter, we used the census accuracies as an error

assessment and propagated it to the computation of k(Goodman 1960; Ku 1966). The standard error of k can be

estimated from the standard error of Nt/N0 following Eq. 2:

sk ¼ 1=t �sðNt=N0ÞðNt=N0Þ

ð2Þ

s Nt=N0ð Þ is the standard error of the ratio of the 2000 to 1982

numbers of breeding pairs censused, t is the number of

years between the two subsequent censuses. s Nt=N0ð Þ was

computed using Eq. 3, as proposed by Ku (1966):

sðNt=N0Þ ¼Nt

N0

�

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

s2N0

ðN0Þ2þ

s2Nt

ðNtÞ2

s

ð3Þ

s N0ð Þ and s Ntð Þ were estimated using an accuracy estimate of

5% for the breeding pair censuses. Based on the standard

error estimate of k, we tested its significance under the

hypothesis of no change in time (k = 0) and using the

Normal law.

Environmental variables

We considered 26 environmental variables that could be

clustered into five main groups: (1) island topography, (2)

yellow-legged gull colonization history, (3) colony status,

(4) island disturbance by human activities, and (5)

anthropogenic food availability in the species foraging

range. These variables were chosen because they can

potentially influence spatial distribution, size, and density

of yellow-legged gull colonies (Fasola and Canova 1992;

Goutner 1992; Scarton and Valle 1996; Bosch and Sol

Popul Ecol (2008) 50:91–100 93

123

1998). As this number of variables is quite high compared

with the number of sites, we used a principal component

analysis (PCA) in order to identify the most correlated

variables and reduce the number of variables to ten pre-

vious to regression analysis. These retained variables

belong to four groups: (i) island topography as described

by (1) elevation (m), and (2) isolation (m), (ii) the colo-

nization history in the study area including (3) the distance

from the initial site of colonization, i.e., Riou island (DInitial

colony; m), and (4) the age of each colony (years), (iii) the

colony status as determined by (5), the nesting area (ha)

used in 1982 (na82), (6) the density of the colony in 1982,

and (7) S82, the ratio between na82 and the total island

area, and finally (iv) the anthropogenic food availability

described by (8) Ton, the annual tonnage in 1982 of the

nearest landfill, (9) DDmin the change, between 1982 and

2000, in the minimum distance to a landfill, and (10) DK

the change in K (the ratio between the annual tonnage of

the nearest landfill and its distance to the colony).

Data analysis

We used multiple linear regressions to model changes in

colony size of the yellow-legged gull. A high number of

explanatory variables relative to sample size generally

causes overfitting in multiple linear regression (MLR). In

order to identify the most important explanatory variables,

we used the best subset method; all potential MLR models

starting from one variable to ten variables were fitted

sequentially and independently (Montgomery and Peck

1982). We searched for the most parsimonious models

(with the smallest number of variables) having a strong

explanative power as defined using criteria parameters: the

adjusted R2, the Mallows’ Cp coefficient (Mallow 1973),

and the residual variance. Contrary to the R2, the adjusted

R2 does not always increase with the number of variables.

The Mallows’ Cp coefficient measures the efficiency of

each subset model relative to the full model. In the case of

least square regression, the Mallow’s Cp is equivalent to

Akaike information criterion (AIC), which is more devoted

to maximum likelihood models. The best model should

have a small Mallows’ Cp, a high adjusted R2, and a low

residual variance. A Cp value (lower or close to the number

of regression parameters including the intercept) indicates

that the there is no significant bias in the model due to

variable redundancy. It is common for the best subset

methods to identify several potential models. In order to

avoid arbitrarily choosing one model, we chose to examine

the best three models (hereafter called concurrent models).

For these three models, a full multiple linear regression

(MLR) was computed, and we used additive diagnostic

checks of the statistical properties to identify the best one.

Collinearity among variables is a common issue with

environmental variables. We used the variance inflation

factor (VIF) to deal with this problem. The VIF measures

how much the variance of an estimated regression coeffi-

cient increases when other correlated variables are included

in the model. Montgomery and Peck (1982) suggest that if

the VIF is >5, the regression coefficients are poorly esti-

mated. Thus, we considered only models with variables

that have VIF <5. The Durbin–Watson statistic was used to

check for the presence of autocorrelation in regression

residuals. Residual distributions were visually checked to

identify variance patterns and normality. Potential outliers

were identified using Studentized residuals. Finally, the R2

calculated from predicted residual sum of squares (PRESS)

(hereafter called predicted R2) was used to estimate the

predictive power of the regression model. Predicted R2 can

prevent overfitting the model and is more useful than

adjusted R2 for comparing models because it allows

assessment of the predictive power for individual values

not included in the model. When the predicted R2 is close

to the adjusted R2, it suggests that the model has a good

predictive ability and is robust to omission of some

observations.

We tested for an island protection status effect on yel-

low-legged gull population changes (Table 1). The

protection status was coded into three groups: (A) no

protection status, (B) protected and restricted access

acquired in the 1990s, and (C) protected and restricted

access to colonies acquired before 1982. ‘‘Protection sta-

tus’’ refers to inclusion in a national park or natural reserve

or territory acquired by the Conservatoire du Littoral. All

three situations involve environmental management of the

island mainly through limitation of human disturbance.

Restricted access refers either to military sites or to nature

reserves where human access is strictly forbidden and only

natural processes are present in the ecosystem. As the

number of islands in these groups is low, we used a non-

parametric median comparison test, Mood’s median test,

which is a nonparametric analog of a one-way analysis of

variance (ANOVA). We tested the hypothesis of no median

k differences between the groups for colony size changes

according to protection status. All the computations were

done using the MINITAB 14 package (� 1972–2004

Minitab)

Results

Population changes between 1920 and 2006

(archipelago level)

In the first decades following their settlement on islands,

the yellow-legged gull populations had a significantly

94 Popul Ecol (2008) 50:91–100

123

continuous exponential growth in the three archipelagos, in

agreement with an annual geometric growth rate k above 1

(Fig. 2). For the populations of the Riou and Hyeres island

archipelagos, the population increase fit with an exponen-

tial growth model for 1920–1982, with an annual estimated

growth rate of 1.061 (CI ± 0.006) and 1.049 (CI ± 0.002),

respectively. For the Frioul archipelago, in keeping with

the first settlement of yellow-legged gulls on the Frioul

archipelago in 1977, the population increase fit with an

exponential growth model for the period 1977–2006, with

an estimated growth rate of 1.102 (CI ± 0.011).

Exponential population growth is always transient, as it

requires unlimited resources. The populations ceased to fit

the exponential growth model during the 1980s for the

older colonies (Riou and Hyeres Islands archipelagos).

During the 1980s, the Hyeres Islands archipelago had a

population peak between 1982 and 1989, with 5,502 and

5,732 pairs, respectively. After this period and between

1989 and 2006, the population decreased at an annual rate

of k = 0.983 (CI ± 0.008). For the Riou archipelago, the

population growth rate slowed down to k = 1.019 (CI ±

0.003) between 1982 and 2006. For the Frioul archipelago

colonies, which were settled three decades ago, population

growth still fits the exponential growth model.

1982–2000: the pivotal period of population change

Based on the census estimates of breeding pair numbers for

20 islands and islets, we calculated an annual growth rate kat the archipelago and island level (Table 1). Based on the

error sampling rate, the standard error of the annual growth

rate was computed and used to test it against the hypothesis

of a null growth rate. At the scale of the studied region,

variability in growth rate existed both at the archipelago

and island levels. The Hyeres archipelago populations

significantly decreased, whereas the Riou and particularly

the Frioul populations continued to significantly increase

Table 1 Results of the censuses performed in 1982 and 2000 on 20 islands and islets of the French Mediterranean shore and the derived annual

growth rates of colony sizes, k, between 1982 and 2000 and their standard errors

Islands and islets Protection

status

Number of breeding pairs

1982 2000 k sk Difference in

annual growth

Frioul archipelago 589 4,323 1.117 0.0002 *

Ratonneau A 304 1,799 1.104 0.0003 *

Pomegues A 265 2,449 1.131 0.0002 *

Tiboulen de Ratonneau A 20 75 1.076 0.0005 *

Riou archipelago 8,750 11,891 1.017 0.0015 *

Riou B 6,000 6,111 1.001 0.0020

Maıre B 800 1604 1.039 0.0010 *

Jarre et Jarron B 800 1845 1.048 0.0009 *

Plane B 1,000 1,965 1.038 0.0010 *

Tiboulen de Maıre B 60 119 1.039 0.0010 *

Grand Congloue B 80 223 1.059 0.0007 *

Petit Congloue B 10 24 1.050 0.0008 *

Verte A 5 113 1.189 0.0001 *

Grand Rouveau A 15 98 1.110 0.0003 *

Hyeres Islands archipelago 5,225 4,299 0.989 0.0024 *

Porquerolles A 1,515 1,275 0.990 0.0024 *

Le Levant C 2,255 1,343 0.972 0.0034 *

Port-Cros C 450 619 1.018 0.0015 *

Bagaud C 650 667 1.001 0.0020

Grand Ribaud A 130 150 1.008 0.0017 *

Gabiniere C 65 104 1.026 0.0013 *

Petit Langoustier C 40 38 0.997 0.0021

Gros Sarranier C 120 103 0.992 0.0023 *

Islands and islets are listed according to their archipelago following a west–east distribution

A no protection status, B protected and restricted access acquired in the 1990s, C protected and restricted access to colonies acquired before 1982

*Annual growth significantly different from 1 (null population change in time) at the risk of 5%

Popul Ecol (2008) 50:91–100 95

123

(Table 1; Fig. 2). All three islands of the Frioul archipel-

ago had a significant population increase during this period,

the highest being Pomegues island, with a mean annual

population increase of 1.131. For the Riou archipelago, all

islands had significant population increases with the

exception of Riou island, which had a quite stable colony

size during this period (Table 1). The highest increase was

1.059 per year for the Grand Congloue island. Intermediate

islands (Verte and Grand Rouveau), located between Riou

and Hyeres island archipelagos, had significant and high

annual growth rates (1.189 and 1.110). For the Hyeres

Islands archipelago, the population decrease of 1.1%

(0.989) per year between 1982 and 2000 was significant

using the error propagation approach. At the island level,

some islands had significantly decreasing populations

(Porquerolles, Le Levant, and Gros Sarranier) or had quite

stable populations (Bagaud and Petit Langoustier), whereas

others had significantly increasing populations (Grand Ri-

baud, Port-Cros, and Gabiniere).

Causes of change in colony size

between 1982 and 2000

The best subset procedure allowed us to examine three

concurrent models with adjusted R2 values of 84.4% (three

variables), 85.8% (four variables), and 86.4% (five vari-

ables), respectively. The Mallows’ Cp coefficients were all

low (1.35, 1.38, and 2.16, respectively). Given the very low

increments of the determination coefficient with the

increase of variables and the fact that the three variables

included in the first model were also included in the two

others, we retained the most parsimonious model with three

variables. The selected variables were DK, GullDens82,

and na82 (see ‘‘Appendix’’ for actual values). The three

variables had significant regression coefficients and VIF

values below 1.2 (Table 2), indicating very low collinearity

between variables. The Durbin–Watson statistic (2.14) and

the distribution patterns of coefficients indicated no evi-

dence of autocorrelation between residuals and residual

normality.

The regression model has the form:

k ¼ 0:626þ 0:465 ðDKÞ � 0:00046 ðGullDens82Þ� 0:000645 ðna82Þ

This means that for colonies with the strongest increase

in colony size over the last 20 years, the increase occurred

on islands with low gull density and limited nesting area in

1982, for which the availability of anthropogenic food

resources had increased during this period either due to the

settlement of landfills nearer the colony or to an increase in

the annual refuse tonnage of the nearest landfill. The

predictive value of the model appeared to remain high, with

a predicted-R2 value of 77.81%, indicating that the model

was stable. Mood’s median test for island protection status

effects indicated a significant difference in colony size

changes between the groups (chi-square = 8.57; df = 2;

P = 0.014). Visual examination of a box plot per status

group (Fig. 3) indicated that colony size changes were

lower when the protection status increased (A > B > C).

Discussion

At the Mediterranean basin level, yellow-legged gull

populations have greatly increased, with an average growth

rate of around 7–9% per year over the past 50 years

(Thibault et al. 1996). In our study area, the populations

00020891069104910291

0

3

6

9

21

51

ogalepihcra uoiR

ogalepihcra serèyH

ogalepihcra luoirF

etaD

#N

estin

gpa

irs (

x 10

00)

Fig. 2 Yellow-legged gull population changes from 1920–2006 in

the three archipelagos of the French Mediterranean (number of

nesting pairs). The focus period (1982–2000) is highlighted by the

grey rectangle

Table 2 Regression coefficient estimates and standard errors for

yellow-legged gull population dynamics from the multiple linear

regression model with three variables

Variable Estimate Standard error t P VIF

Intercept 0.626 7.40 · 10�2 8.46 0.000

DK 0.465 7.09 · 10�2 6.55 0.000 1.2

GullDens82 �4.56 · 10�4 1.17 · 10�4 �3.90 0.001 1.2

na82 �6.45 · 10�4 1.76 · 10�4 �3.65 0.002 1.1

Student t values and P values are used to assess the significance of

estimates

The variance inflation factor (VIF) measures how much the variance

of an estimated regression coefficient increases if variables are cor-

related (VIF < 5 indicates a null or low collinearity of variables)

DK change in the ratio between the annual refuse tonnage in and the

distance to the nearest landfill between 1982 and 2000, GullDens82breeding gull density in 1982 (pairs/ha), na82 the nesting area (ha)

in 1982

96 Popul Ecol (2008) 50:91–100

123

also increased between 1953 and 1982, with a growth rate

of 9% per year (Vidal et al. 2004; this study). A large

change occurred in this pattern of increase in the 1980s.

After 1982, the yellow-legged gull population increase did

not fit with exponential growth for the older colonies (Riou

and Hyeres Islands archipelagos), whereas the populations

that were newly settled on the Frioul archipelago had

exponential growth. Based on these results, 1982–2000

seems to be a pivot point for the population growth of this

species in the area. Currently, the yellow-legged gull

populations appear to be still expanding but at a reduced

speed, with an average growth rate of 2% per year (Oro and

Martinez-Abrain 2007; this study). Some colonies have

probably reached their equilibrium size, such as those at

Riou island, the species’ initial site of settlement in France

(Jaubert and Lapommeraye 1859; Heim de Balzac 1923),

which shows a null growth rate between 1982 and 2000.

Our results confirm the importance of anthropogenic

food resource availability for yellow-legged gull popula-

tion changes since 1982. The populations increased as

availability in anthropogenic food resources increased near

the colony (positive DK). This can be due to the opening of

a new landfill near the colony or the increase in refuse

tonnage in the nearest landfill, or both. The changes in

colony size followed the same trend as the anthropogenic

food resource availability during the last two decades.

From the 1980s, the quantity of domestic waste produced

increased by 5.7% per year between 1982 and 1993, then

by 1.3% per year between 1993 and 2000 (ADEME 2000).

The first landfills opened in our study area in 1974, when

nine yellow-legged gull colonies were already present

among the 20 islands under consideration. The 11

remaining islands were colonized between 1974 and 1983

(‘‘Appendix’’). During the 18-year period examined in our

study, anthropogenic food resource availability increased

in the vicinity of colonies, with the exception of the col-

onies settled on the Hyeres Islands archipelago. Indeed, in

1984, the construction of an incinerator in Toulon city

caused a drop in the annual refuse tonnage of the nearest

landfill for these colonies (Fig. 1). These islands were

exceptional in that they decreased in colony size during the

study period despite having had strong protection status

since 1963 (National Park).

Nevertheless, anthropogenic food resource availability

cannot solely explain the spatial variability of yellow-leg-

ged gull population changes. Breeding site selection by

colonial seabirds is generally the consequence of the pre-

vious year’s breeding success (Danchin et al. 1998), or

linked with the presence of conspecifics (Oro and Pradel

2000; Oro and Ruxton 2001), or both (Brown et al. 1990,

2000). In a recent study on recruitment in expanding gull

populations, Oro and Pradel (2000) demonstrate that local

recruitment and colony size are linked. However, colony

expansion causes increasing resource competition, thus

decreasing the probability of recruitment to the local pop-

ulation after a given threshold, making the founding of a

new colony an increasingly attractive strategy (Forbes and

Kaiser 1994; Oro and Ruxton 2001). The fact that a small

nesting area favored an increase in colony size may corre-

spond to the colonization of small islets subsequent to

saturation of nearby larger islands. Due to the archipelago

structure, the distance between large islands is greater than

that separating large islands from islets (Forbes et al. 2000).

Therefore, the colonization of the nearest sites by breeding

gulls implies that these low-elevation and low-area islands

house relatively large and dense colonies (limited by the

island area). In addition, islets are less disturbed by human

activities (such as tourism), and in the case of the granitic

sites, islets are the only sites where the vegetation cover is

less dense, thus allowing the settlement of relatively large

(several tens of pairs) and aggregated colonies.

The yellow-legged gull had protected status throughout

the European Union from 1981 (Annex II bird species of

the Bird Directive 79/409/CEE). It is interesting to note

that the strong demographic increase within the species in

Europe occurred while it was not yet protected either by the

Bird Directive (79/409/CEE) or French laws. We did not

interpret this observation in a causal way. We only con-

sidered that the protection status of the species did not

induce a population increase. We also tested for a protec-

tion status effect among the colonies. Our results indicated

the opposite effect to that which we were expecting, i.e.,

the better-protected colonies were the ones with decreasing

population changes and vice versa. Again, care must be

taken when interpreting this result, because protection

status certainly does not induce a decrease in population

Protection Status

A

(e

gn

ahc ezis

ynol

oC

)λ

0.9

1

1.2

1.1

CB

Fig. 3 Box plot of changes in colony size from 1982–2000 per

protection status group for the 20 rocky islands of the French

Mediterranean shore. A no protection status, B protected and

restricted access acquired in the 1990s, C protected and restricted

access to colonies acquired before 1982.

Popul Ecol (2008) 50:91–100 97

123

variables. Considering individual colonies leads us to

suggest that an interaction between protection status and

breeding site characteristics is more likely to be responsible

for the patterns found. Most of the limestone islands have

characteristics known to be typical of gull breeding sites: a

large area allowing the settlement of large colonies, a

patchy matorral vegetation providing considerable shelter

and good visibility, and a lack of urbanization (e.g., Scar-

ton and Valle 1996; Bosch and Sol 1998; Kim and

Monaghan 2005). Access to these islands is forbidden or

has been recently restricted. These islands have no or

recently restricted access. In contrast, the granitic islands

have a dense vegetation cover that prevents nesting

throughout most of the area, despite high levels of pro-

tection. Thus, as for the protection of the species, the

protection of the colonies did not appear to be a significant

factor in explaining yellow-legged gull population size

changes. In a recent study, Martinez-Abrain et al. (2003)

came to the same conclusion for the Audouin’s gull.

In conclusion, our results showed that anthropogenic

food availability has strongly influenced the recent local

population changes of the yellow-legged gull on the rocky

islands of the French Mediterranean coast. These results

agree with the results of Oro et al. (2004) dealing with the

Audouin’s gull population changes and trawling discard

availability. Unlike trawling activity, landfills provide a

constant supply of food resources, and we can predict that,

in view of the current urbanization trend on the mainland,

human activities will cause an increase in annual refuse

tonnages if changes in refuse management are not imple-

mented (e.g., closing landfills or replacing them by

incinerators). In addition, the small gull density of some

large islands (Duhem et al. 2008), in addition to the

potential breeding habitat provided by cities surrounding

the older colonies, implies that there are probably nesting

sites available in the area. As a consequence, given no

reduction in landfill activity or in refuse accessibility for

gulls in landfills (Belant 1997), one can expect the region

to sustain the future expansion of a species sometimes

considered as superabundant (Vidal et al. 1998).

Acknowledgments We would like to thank Daniel Oro and an

anonymous referee for invaluable comments on the first version of

the manuscript. We are most indebted to Carey Suehs and Ruth

Menzies for their English editing of the manuscript. Many thanks

are due to the following for their invaluable help in the field: J.

Legrand, the Staff of Port-Cros National Park (especially P. Van-

denbrouck), A. Mante, D. Tatin, Y. Tranchant (Conservatoire-

Etudes des Ecosystemes de Provence) and all volunteers. Funds

and supports were provided by the Conseil Regional de Provence,

Alpes, Cote d’Azur (contracts 99.00012.00 and 2002.16049), the

Port-Cros National Park (contracts 99.006.83400 PC and

00.006.83400 PC), and the ADEME (contract 02.40.038). This

work is part of a scientific program directed by N. Sadoul (Station

Biologique de la Tour du Valat).

Table 3 Topographic features

of the 20 islands and islets

examined in the study (see

Fig. 1 for locations) and

environmental variables

retained by the multiple linear

regression

GullDens82 breeding gull

density in 1982 (pairs/ha), na82nesting area (ha) in 1982, DKchange in the ratio between the

annual refuse tonnage in and the

distance to the nearest landfill

between 1982 and 2000

Islands and islets Number Area (ha) Elevation (m) Isolation (m) GullDens82 na82 (ha) DK

Tiboulen de Ratonneau 1 1.1 30 4,700 55.44 0.36 1.04

Ratonneau 2 95 74 1,800 24.91 51.74 1.04

Pomegues 3 89 86 1,800 21.05 63.58 1.04

Tiboulen de Maıre 4 2.5 47 675 114.05 0.52 1.04

Maıre 5 28 141 50 40.94 19.54 1.04

Jarre - Jarron 6 21.5 57 775 65.27 12.25 1.00

Plane 7 18 22 2,150 134.13 7.46 1.01

Riou 8 90 191 3,000 100.54 59.67 1.01

Petit Congloue 9 0.5 30 3,500 35.3 0.28 1.02

Grand Congloue 10 2.3 50 3,550 71.91 1.11 1.02

Verte 11 15 49 600 1.79 2.79 1.21

Grand Rouveau 12 6.5 31 620 10.81 1.39 1.00

Grand Ribaud 13 16 45 600 72.75 1.79 0.92

Petit Langoustier 14 2.5 12 2,250 64.5 0.62 0.92

Porquerolles 15 1254 142 2,300 84.5 17.97 0.92

Gros Sarranier 16 2.3 26 7,300 158.65 0.76 0.92

Bagaud 17 45 59 7,500 74.95 8.67 0.92

Gabiniere 18 3 62 11,025 145.33 0.45 0.92

Port-Cros 19 640 196 8,200 41.63 12.14 0.92

Le Levant 20 996 140 9,150 17.79 133.34 0.92

Appendix

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123

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