A central objective of population ecology is to understand how populations are spatially structured. An informative way of looking at this is to describe how species abundance varies temporally and spatially and then to identify the mechanisms that can cause synchrony. A pattern common to many taxa is a decrease in synchrony with distance between populations (e.g.
Marcström, Höglund & Krebs 1990; Steen, Yoccoz & Ims 1990; Thomas 1991; Hanski & Woiwod 1993; Pollard, Van Swaay & Yates 1993; Ranta, Lindström & Lindén 1995a; Ranta et al. 1995b; Sutcliffe, Thomas & Moss 1996; Heino et al. 1997; Ranta et al. 1997a,b; Ranta, Kaitala & Lindström 1997c; Ranta, Kaitala & Lundberg 1998; Bjørnstad, Stenseth & Saitoh 1999). Two principal mechanisms have been identified as the possible cause of spatial synchrony. First, dispersal between spatially structured populations (Maynard Smith 1974) and second, the correlated effect of density-independent factors that synchronize populations with the same density-dependent structure, a process referred to as the Moran effect (Moran 1953; Hanski 1991; Royama 1992; Ranta et al. 1997a; Cattadori et al. 1999; Hudson & Cattadori 1999; Koenig 1999). There is also a third mechanism, where predator–prey interactions are responsible for synchrony in prey fluctuations. This may operate through the large-scale effect of nomadic avian predators on local prey populations or as a consequence of specialist predators shifting their attention to an alternative prey during the decline of the main prey (Ydenberg 1987; Korpimäki & Norrdahl 1989; Ims & Steen 1990; Small, Marcström & Willebrand 1993). The relative importance of each mechanism seems likely to depend to some extent on scale. At the local scale, dispersal between populations may well dominate, whereas at higher scales where distances exceed dispersal distances, synchrony is more likely to be caused by correlated stochastic factors (Moran 1953; Pollard 1991; Hanski & Woiwod 1993; Ranta et al. 1995a,b; Sutcliffe et al. 1996; Ranta et al. 1997a, 1998). Synchronized predation may operate at all scales but will only generate synchrony in the abundance of prey when operating at relatively large scales (Korpimäki & Norrdahl 1989; Ims & Steen 1990; Heikkilä, Below & Hanski 1994). While dispersal seems to operate principally at the local scale, recent theoretical studies have shown that populations with cyclic or complex to chaotic dynamics can exhibit broad-scale synchrony by local dispersal, two processes referred to as phase locking and fitness-dependent dispersal (Ruxton 1996; Blasius, Huppert & Stone 1999; Ruxton & Rohani 1999). Distinguishing between dispersal and common stochastic events is an important problem in population biology because it has repercussions on the persistence of local populations and the risk of global extinction (Heino et al. 1997; Heino 1998; Palmqvist & Lundberg 1998).
The only clear way to distinguish between the two principal processes is by experimentally preventing dispersal or by decoupling the environmental density-independent factors. Such experiments on natural populations are logistically difficult. Nevertheless, a study on populations of Soay sheep in the St Kilda archipelago was able to dismiss the possible role of dispersal because sheep populations were separated by several kilometres of lethal Atlantic Ocean (Grenfell et al. 1998). Analyses of population data, with modelling, demonstrated the important role of climatic perturbations in driving the synchronous fluctuations of these closed populations and identified that nonlinear density dependence can have a desynchronizing effect. Of course, dispersal and the Moran effect are not mutually exclusive and it seems likely that both may operate in many systems although the relative importance varies. At the current time the predation hypothesis is considered a special case. The parsimonious hypothesis is that large-scale synchrony can be caused by the Moran effect while local synchrony may be caused by a combination of the two. Ranta et al. (1995a,b) used a simulation model to examine the relative effects of the two mechanisms and concluded that both can independently drive synchrony but that superimposing the Moran effect on dispersal sharply improved the cross-correlations.
The experimental manipulation of natural populations to detect the mechanism of synchrony is usually logistically limited and the only solution is to apply statistical techniques that can indicate the relative importance of the mechanisms. In this paper, we examine this issue by looking at spatial synchrony within and between closely related species inhabiting the same area. We postulate that synchrony between species, particularly those inhabiting similar habitats, is probably caused by correlated environmental factors. By investigating the patterns of synchrony within and between species and by applying simple models, we argue that we should be able to obtain an estimate of the relative importance of dispersal and common environmental stochasticity.
The alpine gamebirds provide a suitable data set to examine problems of spatial synchrony. All the species are restricted to discrete mountain groups and exhibit a distinct altitudinal distribution, with the woodland grouse at the lower altitudes and the open-habitat species on the mountain plateaux. The data set is based on long-term hunting statistics, which provide a reasonable estimate of changes in abundance (Cattadori & Hudson 1999; Cattadori et al. 1999). Population data on gamebirds are particularly useful because they are collected in the autumn and exhibit large-scale fluctuations in abundance, which are driven principally by changes in productivity and particularly the mortality of chicks during the first few weeks of life. While a number of workers have highlighted the importance of climatic conditions in influencing survival of gamebird chicks, it is apparent that other factors, such as the availability of invertebrate food, the condition of the female or the effect of predation, will also play a role (Hudson 1986; Potts 1986; Potts & Aebischer 1994). Even so, harsh weather conditions are likely to have either a direct or indirect negative effect on the survival of chicks and we can expect these stochastic effects to be one of the principal synchronizing forces in gamebirds. The climatic data for Trentino is aggregated into different macroclimatic areas and this distribution avoids some of the confounding interactions between correlated weather and distance (Boato, Arrighetti & Osti 1988; Gafta 1994; Cattadori et al. 1999).
In this paper we specifically address three questions:
1. Do closely related galliform species exposed to common stochastic events exhibit similar patterns of synchrony?
2. How does synchrony vary between species relative to habitat differences?
3. Is dispersal or common stochastic events the cause of spatial patterns of synchrony observed in galliforms?
We reproduce the spatial patterns of synchrony using two spatially explicit models. First, we estimated both the dispersal and the effect of environmental perturbations using the Ricker model and estimating functions method (Godambe 1991; Lele, Taper & Gage 1998). Second, we applied the Ricker model assuming a priori the stochastic effect of a climatic variable and then estimated the dispersal rate and dispersal range of each species using the modifications applied by Ranta et al. (1995b, 1997c).