All population changes result from births, deaths, emigration and immigration. Dispersal contributes to spatio-temporal variation in population size, and is therefore a process to consider in studies with conservation and management implications (e.g. Nichols et al. 2000; Cooch, Rockwell & Brault 2001; Hanski 2001; Oro & Ruxton 2001). In addition, dispersal and spatio-temporal variation in local population size and growth rates within metapopulations are components of the selective environment of life history traits (Stearns 1992); they are also relevant to studies of the evolution of life history traits other than dispersal itself (Caswell 1982; Olivieri & Gouyon 1997). Dispersal (and the resulting gene flow) is one of the mechanisms underlying evolution (Barton 2001). However, as emphasized by Hanski (1999) and Bennetts et al. (2001), there is a long history of investigation of reproductive parameters and survival probability but dispersal still constitutes one of the major gaps in our knowledge of ecological dynamics. MacDonald & Johnson (2001) pointed out that despite the ‘prodigious’ number of models of dispersal evolution (Johnson & Gaines 1990), ‘there remains an almost complete absence of empirical data on dispersal and other relevant behavioural parameters in recent population extinction studies’. The very large literature on metapopulation dynamics lacks empirical bases (Spendelow et al. 1995; Hanski 2001; MacDonald & Johnson 2001).
Until recently, practical difficulties in field studies (e.g. Spendelow et al. 1995; Koenig, Van Vuren & Hooge 1996) and limitations of available approaches to statistical inference about movement of individuals among locations were two major obstacles in investigations of dispersal processes (Bennetts et al. 2001). Recent development of approaches to estimating movement probability using data from marked animals has considerably increased our ability to address dispersal (e.g. Nichols et al. 1992; Clobert 1995; Nichols & Kendall 1995; Nichols 1996; Nichols & Kaiser 1999; Bennetts et al. 2001; Williams, Nichols & Conroy 2002). Logistical difficulties still remain substantial: estimation of movement probability within metapopulations requires careful design and the study of marked individuals in several locations simultaneously. In species with high dispersal ability, logistical limitations seldom allow investigators to monitor many locations simultaneously, especially locations far away from the main study area (Koenig et al. 1996). As a result, in spite of development of robust approaches to statistical inference about movement in the 1970s (e.g. Arnason 1972, 1973) and implementation of these approaches in flexible software programs in the 1990s (e.g. Brownie et al. 1993; Lebreton, Almeras & Pradel 1999; White & Burnham 1999), empirical studies of dispersal among patches using robust approaches to estimating movement probability are still few (but see Hestbeck, Nichols & Malecki 1991; Brownie et al. 1993; Spendelow et al. 1995; Nisbet & Cam 2002; Senar, Conroy & Boras 2002).
We used multistate capture–recapture models (e.g. Nichols & Kendall 1995) to estimate movement probability. In long-lived species, it is natural to assume that individuals have to make decisions concerning fidelity to their previous breeding site every year. As individuals are likely to experience changes in environmental characteristics throughout life, fixed dispersal strategies (e.g. individual dispersal behaviour being independent of environmental conditions) are unlikely to be favoured by natural selection (e.g. Ronce et al. 2001). In mobile long-lived animals dispersing actively, dispersal can be viewed as a ‘decision making’ problem (i.e. ‘to stay or to leave’; e.g. Danchin, Boulinier & Massot 1998; Doligez et al. 1999; Brown, Bromberger & Danchin 2000; Murren et al. 2001; Serrano et al. 2001). It has been hypothesized that decisions are state-specific (i.e. depend on the individual state, such as condition, previous breeding success, breeding habitat and other environmental factors; Mangel & Clark 1988; McNamara & Houston 1996; Danchin et al. 1998; Clark & Mangel 2000). Recent syntheses about dispersal highlighted the growing attention the questions of individual plasticity and condition-dependent dispersal are receiving (Danchin, Heg & Doligez 2001; Ims & Hjermann 2001; Ronce et al. 2001; Serrano et al. 2001; Sutherland, Gill & Norris 2002; Williamson 2002). Our second objective was to assess several hypotheses about factors potentially influencing movement probability between two colonies of Audouin's gulls in the Mediterranean.
We addressed several hypotheses about movement probability. We followed the approach specified by Nichols & Kendall (1995) and Spendelow et al. (1995), and addressed the hypothesis of an influence of year on movement probability, and also the hypothesis that movement probabilities among colonies were equal. When the individual's perspective is considered, the evolution of dispersal can be tackled within the framework of ‘habitat selection’, the broad scope of which encompasses both the decision of leaving a site and the choice of a new one (Ronce et al. 2001). A key question is how individuals make decisions concerning fidelity to the previous breeding site, and if they decide to move, selection of a new one (Danchin et al. 2001; Stamps 2001).
One of the main predictions of the ‘ideal free habitat selection theory’(the cornerstone of many studies of habitat selection; Fretwell & Lucas 1970) is that natural selection should favour dispersal tactics where moving leads to increased realized fitness (i.e. habitat selection should be shaped by fitness maximization; Holt & Barfield 2001). Fretwell & Lucas (1970) hypothesized that movement between years t and t + 1 depends on the relative realized fitness of different locations in year t + 1. We might expect individuals in a habitat with lower realized fitness to move to a habitat with higher realized fitness. Densities in the various locations are expected to change as well as realized fitness in each habitat, and eventually realized fitness is equilibrated. Because of the numerous assumptions this theory relies on (e.g. individuals have perfect knowledge of their environment, there is no cost of moving, etc.; Holt & Barfield 2001; Stamps 2001), the scenario leading to the ‘ideal free distribution’ of individuals in space developed by Fretwell & Lucas (1970) is unlikely to be observed in the wild (Nichols & Kendall 1995). However, the seminal idea that habitat selection is shaped by fitness maximization leads to some specific predictions. In our study area, breeding success (measured by mean fledging success) was highly variable in the two colonies (e.g. Oro et al. 1996, 1999; see Results) over the study period, there was no parallelism in temporal variation in local mean success and mean success was never equal in the two colonies. In such a situation, we might expect asymmetrical time-varying movement probability between the colonies.
A dispersal event can be viewed as two consecutive events: the decision regarding fidelity to the previous breeding site and, if individuals disperse, the decision regarding a new breeding site. The hypothesis that fitness maximization shapes habitat selection tactics leads to the question of how individuals can assess fitness prospects in different potential locations. It has been hypothesized that individuals use their own breeding success and the success of conspecifics as cues to assess expected location-specific fitness (Danchin et al. 1998; Doligez et al. 1999; Brown et al. 2000; Serrano et al. 2001), and that the decision regarding the location where they will breed in year t + 1 is made in year t.
This hypothesis relies on two assumptions: (1) that individuals gather information on breeding success in various potential breeding sites in year t (e.g. through prospecting); (2) breeding success in a given site/year is a good indicator of fitness prospect in this location in the following year (i.e. habitat quality is predictable at the scale of two consecutive years; Danchin et al. 1998). If the above assumptions are met, we might expect the probability that individuals leave a location to be negatively correlated with breeding success in the colony of origin (used as a surrogate for realized fitness) in year t. We addressed this hypothesis using ultrastructural models (e.g. Nichols & Kendall 1995; Spendelow et al. 1995) where the probability of moving between years t and t + 1 is a function of the mean breeding success in the colony of origin in year t. We assessed two variants of this model, one with distinct baseline probabilities of leaving for the two colonies, the other with a common baseline probability of leaving. The former corresponds to a situation where the baseline probability of leaving a colony depends on the colony, the latter to a situation where this baseline probability is similar in all the colonies. We might also expect the probability of moving to a colony to be positively correlated with mean breeding success in that colony in year t. These hypotheses are not mutually exclusive. We addressed the relative importance of mean breeding success in the colony of origin and the destination colony in year t on movement probability in individuals that dispersed between years t and t + 1.
Breeding success per se may not be sufficient to account for movement. Indeed, fitness cannot be increased by leaving a location (even with low productivity) if there is no other location with higher expected fitness. We might rather expect movement between the two colonies to vary over time and to depend on differences in fitness (i.e. fitness gradient) among locations (Bull, Thompson, & Moore 1987; McPeek & Holt 1992).We used the approach specified by Nichols & Kendall (1995) to investigate the influence of the fitness gradient on movement probability (see>Results).
In addition, local mean breeding success in year t may not be a good indicator of fitness prospect in the following year, and individuals may rely on other cues at the beginning of year t + 1 to assess local fitness prospect in that year. In this case we might expect local breeding success in year t + 1 to be more important than in year t. Consequently, we considered not only the influence of the fitness gradient or breeding success in each location assessed in year t on movement probability, but also the influence of these factors assessed in year t + 1.
We also addressed the influence of colony size (i.e. number of breeding pairs in year t) on movement probability. The ideal free distribution (IFD) (Fretwell & Lucas 1970) relies on the idea that realized suitability varies with the density of conspecifics established in the same location: this theory assumes a negative density-dependent effect. However, density may influence movement probability for other reasons. For example, a limited number of breeding sites may simply prevent individuals from establishing in a location. It has also been hypothesized that there is a positive density-dependent effect in small size colonies (e.g. Stamps 2001). In addition, according to the conspecific attraction hypothesis (Smith & Peacock 1990; Ray et al. 1991), in the absence of density-dependence larger colonies are expected to attract more individuals than smaller ones (see also Forbes & Kaiser 1994). One of the colonies in the study area remained much larger than the other throughout the study.
Finally, we investigated the influence of the number of yellow-legged gulls (L. cachinnans) established in the same locations as Audouin's gulls on the probability of moving in the latter. Predation on eggs and chicks by yellow-legged gulls influences the overall breeding success in Audouin's gulls, and numbers of yellow-legged gulls are suspected to influence disturbance rate (e.g. Oro 1998; Martínez-Abraín et al. 2003a).