The modelling approach
There are several methods to analytically combine data on species distributions, traits and the environment. The combinations of these three elements are the classical domain of ‘4th corner problems’ (Legendre et al., 1997; Legendre & Legendre, 1998), where an environment by trait matrix (the fourth corner) is estimated by three other matrices: (1) a species-by-locality matrix, (2) a species-by-traits matrix and (3) an environment-by-locality matrix. Legendre et al. (1997) solve the problem by combining these three matrices through multiplication. Other possibilities are, for example, the combination of the three matrices through multivariate approaches such as coinertia analysis (Dray et al., 2003) or the use of linear models with traits as explanatory variables to explain species niche breadth (Thuiller et al., 2004) or species responses (Lososova et al., 2004) of prior multivariate analyses. However, we analysed the spatial distribution of the relative frequencies of traits, and thus have to: (1) treat the grid cells as the unit of observation, since the composition in each grid cell is one observation on the relative frequencies of the pollination types; (2) allow for the unit sum constraint of compositional data; (3) allow for spatial autocorrelation in model residuals, since the trait compositions vary smoothly over space. The statistical framework as proposed in this paper meets the above requirements.
Allowing for the spatial dependency of the compositions by means of a conditional autoregressive model proved necessary to obtain an adequate fit of the model to the data. Models that did not allow for spatial autocorrelation in the compositions proved too optimistic in the uncertainty of the model predictions, as reflected by the narrow credible intervals that excluded most of the observed data points. This illustrates the fact that models that ignore spatial autocorrelation are susceptible to type I errors because they are overoptimistic about the precision of the estimated values of the parameters (Cressie, 1993; Legendre, 1993; Anselin & Bera, 1998). By contrast, the estimated uncertainty in the parameter estimates and predictions from the model with spatial smoothing were realistic, making any inferences on the effect of covariates on the composition of plant communities more credible. The composition of plant assemblages, as derived from species atlases with a coarse spatial resolution, will often vary smoothly over large geographical areas. However, since the spatial distribution of most covariates and the response variable will not overlap entirely, allowing for spatial autocorrelation in the model residuals is crucial when modelling plant community compositions. Few examples exist in the literature on applications of statistical methods that are suitable for spatially correlated compositional data, except the work by Billheimer et al. (1998) and Billheimer et al. (2001). In this paper we demonstrate the application of these methods in the analysis of community composition data as derived from species atlases on a coarse spatial grid. In addition, our study demonstrates the usefulness of GeoBugs (Thomas et al., 2004), which greatly facilitates the implementation of these relatively nonstandard geostatistical models.
Biogeographic patterns of pollination types on meso-scale
The shifts in compositions of pollination types revealed in our study were rather small across Germany (Fig. 1). In addition, the resolution of our analysis was rather coarse (c. 130 km2) and pollination effects are most likely to operate on small scales (Kunin, 1992). Nevertheless, we still were able to detect a clear distributional pattern of pollination types and a response to environmental factors. Though the pattern is striking, it is worthwhile to note that across Germany entomophily is still almost twice as frequent as anemophily or autogamy; there is only a relative shift of the pollination types in space.
At this spatial scale and resolution, many of the previously published results on relationships between the spatial distribution of the environment and pollination types cannot be confirmed. The estimated increase in insect pollination, and decrease in wind and self-pollination, from low to high altitudes is even the complete opposite of earlier hypotheses or observations (Regal, 1982; Whitehead, 1983; Richards, 1997).
A number of processes may be involved in determining the distribution of pollination types: evolution, weather and climate, physical properties of vegetation types, species interactions and history including human influence. We will not discuss the evolution of pollination modes since most of the German flora evolved outside of the area and colonized it after the last glaciation.
Insect pollination is typically associated with zero to low wind speed, medium to high humidity and infrequent to common precipitation (Regal, 1982). In our model, distribution of entomophily may best be explained by human land use as a consequence of topography, geology and the absence of strong winds. The most species-rich areas, with a high number of colourful flowering entomophilous species, are calcareous pastures and meadows which are extensively used in mid-altitudes and naturally open vegetation above the timberline in the Alps (Ellenberg, 1996). Thus they are situated in the mid- to high-altitude mountain areas (Ssymank et al., 1998) where agriculture could not be intensified as much as in the lowlands or is characterized by extensively used seasonal mountain pastures. Unfortunately, only proxy variables are available, but the combination of altitude and area of limestone support the ideas mentioned above, as does the distribution of the respective habitats (see Ssymank et al., 1998). Clearly, because high wind speeds impede insect flight, this covariate is negative correlated with insect pollination. It is not easy to explain the positive relationship between the area of arable fields (which are usually species poor) with proportion of insect pollination, as many weed species of fields are self- or wind-pollinated (Baker, 1974). However, the predicted gradient is small with relatively large uncertainty.
One of the best predictors for the spatial distribution of proportions of wind pollination is wind speed, although it is known that the optimum for wind-pollination is at low to moderate wind speed (Whitehead, 1983). Terminal velocities of pollen for most anemophilous species range from 0.02 to 0.06 m s−1 (Whitehead, 1968). Wind speed in the studied area ranges from 1.8 to 8.9 m s−1 at 10 m above ground level. The average wind speed within plant communities is about the same as those in our data at 10 m above ground level (1–10 m s−1, Whitehead, 1983 citing Tauber, 1965, Geiger, 1966). Wind speeds are therefore c. 100 times faster than needed for pollen dispersal and should not be a limiting factor. However, pollinators do have difficulties in flying from flower to flower and pollinating when wind speed is high, so that the observed pattern could result from a decrease in insect pollination. Furthermore, it seems that the distribution of anemophilous plant species may also be explained by factors (covarying) other than just wind. An altitudinal increase in anemophily seems to be a wide spread pattern across many regions of the world and across several taxa, as a result of low pollinator availability (Regal, 1982; Berry & Calvo, 1989; Anderson et al., 2001). In Germany, however, there is also an increase of anemophily in the lowest areas which are the northernmost ones. Although this seems to fit in with the latitudinal trend of increase in anemophily (Regal, 1982), which is understood as a result of unfavourable climatic conditions, the climatic variables – when exchanging altitude with temperature in our model – do not support this idea.
Wind pollination is facilitated by open vegetation (Culley et al., 2002). Thus, broader-scale vegetation patterns may also influence trait composition, especially if open, graminoid-dominated vegetation types exist. Most of Germany is part of the temperate forest biome, particularly beech forest. However, grasslands of various types exist mainly as secondary anthropogenic vegetation throughout the country. The most species-rich types of grassland on limestone occur mainly in the mid-altitudinal ranges of Central and Southern Germany. These grasslands are, however, especially rich in insect-pollinated species, which may partly account for the high level of insect pollination at mid- to high-altitude ranges found in our analyses (see above). Vegetation types which strongly differ in their abundance across Germany are bogs and fens that occur both in the climatically humid areas in the northern lowlands and in the South in the peri-alpine and alpine zone but are rare or absent in the central mid elevation parts (Ellenberg, 1996; Succow & Joosten, 2001). In bogs, a high species richness of mostly wind-pollinated Cyperaceae is found, at least part of which are biogeographically restricted within Germany to these two areas and thus may contribute to the relative minimum of wind-pollination in the central, mid-altitude parts of Germany. In addition, the distribution of these bogs across Germany is partly caused by and partly covaries with the last Pleistocene glaciation (Liedtke & Marcinek, 2002). This historical factor can potentially contribute to the mid-altitude minimum of wind-pollination as both glaciated regions (the northern lowlands and southern alpine areas) still may have imprints on trait composition owing to glacial relict species.
Self pollination is the pollination mode that was most scattered across Germany, only being less frequent in the south-east. The reproductive assurance hypothesis (Baker, 1955) states that selfing is a selective advantage when pollinators are absent (e.g. owing to poor or unpredictable climatic conditions, frequent disturbance or during colonization; see also Schoen et al., 1996; Kalisz & Vogler, 2003; Kalisz et al., 2004). The areas of highest selfing frequencies are along the North Sea coast, the Pleistocene lake areas in the north-east and some parts of the Elbe and Rhine valley. All these are regions where natural disturbance is high (e.g. owing to flooding events and/or storms). The large river valleys are also known to be especially rich in alien species (Planty-Tabacchi et al., 1996; Deutschewitz et al., 2003) which are more frequently self-pollinated than native species (Klotz et al., 2002).
The percentage of self-pollination was clearly found to decline with increasing altitude (Fig. 3a). This finding strongly contrasts with the classical expectation based on the reproductive assurance hypothesis that selfing should be selected for under unfavourable environmental conditions such as high altitudes or latitudes in which pollinator service may be uncertain (Bliss, 1962; Richards, 1997). This view, however, seems to have been based on the premature adoption for alpine floras of the suggested role of autogamy and apomixis in arctic floras (Packer, 1974). Empirical evidence for a declining role of insect pollination may be biased towards particularly sensitive and taxonomically narrow groups of insect pollinated species, like orchids pollinated mainly by Lepidoptera (Jacquemyn et al., 2005). The decline of selfing species with increasing altitude may not be related to the breeding system itself, but may result from the predominant annual life cycle of selfing species. Thus, they depend on the successful completion of the life cycle within one season. This makes them more susceptible to unfavourable and variable climatic conditions than the preferentially outcrossing perennials which can accumulate scarce resources over time and endure unfavourable conditions.
Indeed, a number of studies point to the maintenance or predominance of outcrossing breeding systems at high altitudes of temperate regions (Gugerli, 1998; Körner, 2003). Unlike selfing, outcrossing breeding systems ensure the maintenance of high genetic variability at population level which is considered a prerequisite for long-term persistence in stochastic environments (Lande & Shannon, 1996). Indeed, many high-elevation plants combine outcrossing breeding systems that ensure the maintenance of genetic variability with clonal propagation, allowing persistence and reproduction of successful genotypes under harsh environmental conditions. Selfing was also negatively affected by percentage grassland. Species-rich grasslands are characterized by strong competition among species resulting in low numbers of weak competitors like annuals which mostly are selfing species (Aarssen, 2000). Wind speed was weakly positively correlated with selfing. This, however, is probably a result of the strong decrease of insect pollination.
A number of factors and processes that may influence the distributional patterns of pollination modes cannot be tackled by our analysis. First, pollination is not static within a species but may vary both in space and time in adaptation to local conditions. Thus there is not only a shift between species and in species composition but also a shift within species and populations from outcrossing to selfing under adverse environmental conditions (Kalisz et al., 2004); this is also known for a number of European species (Couderc, 1978). However, such processes could not be recognized at the scale of our analysis and the species data at hand. Second, pollination is not necessarily needed for reproduction if species can reproduce clonally. Thus, despite having a specific pollination mode and breeding system, the distribution of clonal species may be independent of functioning of their sexual system (Hollingsworth et al., 1998). Third, and most important, the patterns of pollination type distribution may be biased by phylogenetic effects (i.e. closely related species of one grid cell may dominate a pattern against species from distant clades of a phylogeny). Unfortunately, we are not aware of any way of incorporating comparative methods (Harvey & Pagel, 1991) for compositional data in our context and that could be applied to > 2700 ‘samples’.
There was some caution raised by Quinn et al. (1994) when interpreting spatial patterns in the abundance of pollination types that are extrapolated to larger scales (e.g. 10 × 10 km resolution). Nevertheless, at a scale of 10′ longitude × 6′ latitude, we were successfully able to show a distinct pattern in the spatial distribution of the composition of pollination types across Germany, which we were able to explain using a set of environmental variables by effectively employing a novel statistical method which is applied (to our knowledge) for the first time to species distribution atlases.