## Introduction

The relationship between the number of species and the area sampled is one of the best documented patterns in community ecology (Williamson 1988; Durrett & Levin 1996). Although the fact that larger areas contain more species than smaller ones is quite obvious, there is no consensus about the exact form of the species–area relationship (hereafter SAR), and the shape and slope of the SAR have remained largely unexplained. The relationship is mostly linear under a log–log transformation, following a power equation (Arrhenius 1921; Rosenzweig 1995), but it may follow an exponential (Gleason 1922; Lennon *et al*. 2001) or logistic equation across some spatial scales (He & Legendre 1996). Moreover, its slope can vary considerably (Connor & McCoy 1979) and this variability can be partially accounted for by the geographical situation: the slope is higher for isolated areas than for areas nested within one continuous mainland, the highest slope of the relationship being attained by comparison among different continents or other biotic provinces (Rosenzweig 1995). Whereas the differences in species richness among isolated pieces of land are probably strongly affected by the dynamics of colonization/extinction or speciation/extinction (MacArthur & Wilson 1967; Rosenzweig 1995), the SARs within continuous mainland are affected by the factors that determine the spatial distribution of individuals (He & Legendre 1996, 2002).

There are principally three factors related to the spatial distribution of individuals that affect the shape and slope of SARs. The first is the sampling effect: because the majority of species are rare (Preston 1948; Gaston 1994), most will not occur in all of the sampled areas and will be sampled only within larger ones, even if their spatial distribution is random. Therefore the sampling effect itself is capable of producing monotonically increasing SARs (Preston 1962), although it is not sufficient for generating either power-law or realistic slopes of SARs (Leitner & Rosenzweig 1997). The second factor is habitat heterogeneity (Rosenzweig 1995): larger areas host more habitat types, and thus enable coexistence of more species associated with particular habitats. Habitat heterogeneity potentially affects the spatial clustering of individuals, but this can be affected as well by spatial population dynamics, including the dynamics of local colonization and extinction (Hanski & Gyllenberg 1997) or aggregative behaviour (Taylor, Woiwod & Perry 1978). Thus, spatial population dynamics of species may be considered as a third major factor affecting SARs.

The processes contributing to the shape and slope of mainland SARs have been tested only indirectly. Whereas the pure sampling effect could be rejected quite easily, because if the abundance distribution of species is known it gives exact predictions concerning the shape and slope of SARs, testing the other effects (habitat heterogeneity and spatial population dynamics) is complicated because it is not clear which patterns they should produce. For example, an increased number of habitats with area must surely influence SARs, but habitat heterogeneity itself gives no quantitative prediction of their form; it is not clear why habitat heterogeneity should increase with area in such a way that power-law SARs with particular slopes emerge. The effect of heterogeneity has therefore been tested mostly by partialling out area and the diversity of habitats (see Boecklen 1986), with the almost invariant conclusion that habitat diversity indeed correlates with number of species even if area is controlled (Rosenzweig 1995; Gaston & Blackburn 2000). In contrast, there is theory that predicts the quantitative parameters of SARs on the basis of metapopulation dynamics (Hanski & Gyllenberg 1997), but the assumptions do not seem appropriate for many situations other than the archipelagoes of isolated islands or sufficiently separated and discrete habitat patches (Gaston & Blackburn 2000).

As all of the factors mentioned probably affect the spatial distribution of species, it is not easy to disentangle them and evaluate their importance separately. One way to do this would be to assess the exact mathematical properties of empirical SARs and to compare them with theoretical predictions based on the hypothesized mechanisms (He & Legendre 1996). However, because it is not very clear which theoretical prediction could be derived from particular mechanisms, and because distinguishing individual mathematical forms of the relationship is extremely difficult (Connor & McCoy 1979), this approach is very limited. The second approach consists in building models that include only particular factors of concern, and comparing them with observed SARs (regardless of their exact mathematical form) to assess which of these factors are actually sufficient for producing observed SARs. This approach, however, depends strongly on the availability of data related to the particular mechanisms, i.e. besides data on the real spatial distributions of species, data are also required on population abundances and the spatial distribution of suitable habitats of individual species. As these data are available for birds in the Czech Republic (see Storch & Šizling 2002), we could test to what extent the sampling effect, habitat heterogeneity and population aggregation that is not attributable to habitat heterogeneity are responsible for SARs in this particular situation.