Traits of plant species have been used in numerous studies to reveal general ecological patterns irrespective of species identity (McIntyre et al., 1999; Rusch et al., 2003). The relationship between plant functional traits and the environment at large geographic scales has recently received considerable attention (MacGillivray et al., 1995; Skarpe, 1996; Díaz et al., 1999; Wright et al., 2004; Traiser et al., 2005; Wright et al., 2005; Mayfield et al., 2006). Although pollination is a key process in ecosystem functioning, as it plays an important role in plant reproduction, it is often neglected (e.g. Weiher et al., 1999). There are three pollination types which account for the majority of plant species in temperate ecosystems: wind pollination (anemophily), insect pollination (entomophily) and self pollination (autogamy).
Many studies focused on the relationships among different pollination types, mainly from an evolutionary viewpoint, albeit often in an ecological context (Midgley & Bond, 1991a, 1991b; Kevan et al., 1993). Often, such studies focused on either few species within a genus or family (Berry & Calvo, 1989; Tamura & Kudo, 2000; Dupont, 2002; Kalisz & Vogler, 2003) or specific groups of plants such as trees and shrubs (Regal, 1982). Some studies investigated biogeographic trends or relationships between specific pollination types and environmental parameters. In moist temperate forests wind pollination has been found to increase with latitude and altitude and to decrease with plant species richness (Regal, 1982; Whitehead, 1983). Wind pollination also depends on factors other than just wind, such as humidity, rainfall and temperature (Culley et al., 2002). Rainfall has a negative effect as it washes the pollen away (Regal, 1982). The optimum conditions for wind pollination are low to moderate wind speed, low humidity and infrequent precipitation (Whitehead, 1983; Culley et al., 2002). Too high wind speeds may hinder stigmatic pollen capture (Niklas, 1985). Insect pollination is typically associated with zero to low wind speed, medium to high humidity and infrequent to common precipitation (Regal, 1982). Obviously, insect pollination is restricted to regions where insects could thrive. However, it is difficult to get data on insect abundance on biogeographic scales. Furthermore, entomophily increases with plant species richness (Whitehead, 1968; Regal, 1982).
Selfing is regarded as a method of reproductive assurance (Baker, 1955; Schoen et al., 1996; Kalisz et al., 2004). Selfing should be especially favoured under variable pollination environments (Kalisz et al., 2004) or poor climatic conditions where pollinators or mates are absent (Baker, 1955).
There are other factors in addition to climatic variables or the direct physical environment that may have an influence on the composition of pollination types. Wind pollination is favoured by open vegetation while insect pollination occurs in open to closed vegetation (Culley et al., 2002). Conversely, many tree species of temperate forests are wind pollinated (Regal, 1982).
The relationships and biogeographic trends reported in these studies suggest that pollination types are differentially selected for by different ecological and environmental conditions. If this is a general rule, one would expect to find different pollination types displaying different spatial patterns, based on the geographical variation of the underlying environmental factors. This in turn should allow the variation in pollination types not only to be mapped and their spatial structure to be analysed, but also to develop proper statistical models that could explain and predict such patterns. Such an approach, which aims at identifying the underlying environmental drivers of the geographic distribution of traits, is different from the approach generally applied in studies of functional traits (i.e. focusing on dynamic processes such as responses of trait composition to management). Hitherto, actual geographical distribution of selected traits was taken rarely into account (e.g. Traiser et al., 2005) and we are not aware of any such analysis on the distribution of pollination types.
Here, we used a biogeographic approach and analysed the distribution patterns of a trait (pollination) that we derived from a comprehensive mapping scheme and which can be related to environmental data at the same spatial scale. The scale of our study is different from previous analyses of pollination types (Regal, 1982; Whitehead, 1983) because our analysis is based on a greater number of species (several thousand species) and their occurrence patterns over a large geographical extent and at a coarse spatial resolution. We analysed the distribution patterns of the relative frequencies of different pollination types in Germany at a 10′ longitude × 6′ latitude resolution (c. 130 km2) in a spatially explicit statistical framework.
Analysing spatial maps of relative frequencies (compositions) of species or traits poses certain critical statistical challenges. First, the proportions of traits or species in different groups add up to 100% (unit sum constraint of compositions). Therefore, an increase in relative frequency of one group results in the decrease in relative frequency of one or more other groups (Aitchison, 1982, 1986; Billheimer & Guttorp, 1995; Billheimer et al., 2001). A second statistical challenge is to deal with the potential spatial autocorrelation structure in the data or model residuals. The presence of spatial autocorrelation in a data set may lead to several problems. In the presence of positive spatial autocorrelation, errors are not independently distributed, which violates the basic assumption of usual linear modelling techniques (Haining, 2003). This will lead to an overestimation of degrees of freedom and Type I errors may strongly be inflated (Legendre, 1993). Furthermore, the effects of the explanatory variables may be estimated incorrectly (Cressie, 1993; Anselin & Bera, 1998). In this paper we present a novel statistical approach that enables us to relate maps of trait compositions with maps of the environment, based upon the breaking of the unit sum constraint using log-ratios of proportions. Equally importantly, the approach applied here enables us also to account for the potential biases in the pollination traits models stemming from spatial autocorrelation by the spatial smoothing of model residuals using a conditional autoregressive model. While a similar methodology was introduced by Billheimer et al. (2001) for the modelling of species composition data gathered at a number of sampling stations, this is to our knowledge the first application of these techniques in the analysis of geographical maps of species’ trait compositions. Such maps will become more commonly available as species atlases are coupled with databases of species traits.
The aims of this study can be summarized as (1) exploring whether pollination types will yield spatially structured distribution patterns; and (2) testing whether mapped environmental variables can account for those patterns by modelling the distribution of distribution of pollination type composition using a Bayesian framework in the presence of spatial autocorrelation.
As suggested by other studies (Whitehead, 1968, 1983; Regal, 1982; Niklas, 1985; Culley et al., 2002; see earlier) we expect that altitude, temperature, precipitation, wind speed and specific geological substrates associated with species richness (Kühn et al., 2003) may influence the composition of pollination types. Specifically, we hypothesize that: (1) the proportion of insect-pollination increases with increasing temperature and area of lime and loess subsoil, and decreases with increasing precipitation and wind speed; and (2) the proportion of wind pollination increases with altitude, open vegetation (e.g. grasslands and arable fields) and moderately with wind speed, and decreases with increasing precipitation. We expect that (3) the spatial variation in the proportion of self-pollination will be least well explained, because selfing results from a lack of other opportunities of pollination or as a reaction to unpredictable or highly varying environmental conditions, (i.e. it should increase with altitude and in disturbed regions).