Patterns of bryophyte and vascular plant richness in European subalpine springs

The diversity of spring habitats can be determined not only by local environmental conditions, but also by large-scale biogeographical effects. The effects can differ across various groups of organisms. We compared α-, β- and γ-diversity patterns of bryophytes and vascular plants of (sub)alpine springs in three contrasting mountain ranges: Alps (Switzerland), Balkans (Bulgaria), Western Carpathians (Slovakia, Poland). We used univariate and multivariate statistics to test for the effects of pH, conductivity, altitude, slope, mean annual temperature and annual precipitation on diversity patterns of both taxonomic groups and compared diversity patterns among the regions for particular pH and conductivity classes. We identified acidophyte and basiphyte, calcifuge and calcicole species using species response modelling. All regions displayed significant relationship between conductivity and α-diversity of vascular plants. Bulgaria showed the highest α-diversity of vascular plants for the middle part of the conductivity gradient. For both taxonomic groups, the β-diversity in the middle part of gradient was highest in Swiss Alps. The total species pool was lowest in Bulgaria. The percentage of basiphyte and calcicole species was highest in the Alps. In (sub)alpine springs, mineral richness was a better determinant of vascular plant α-diversity than pH, and the extent of the alpine area did not coincide with α-diversity. Observed inter-regional differences in diversity patterns could be explained by the different proportion of limestone bedrock and different biogeographic history. The differences in α-diversity between both taxonomic groups are presumably result of the different rates of adaptation processe

Modelling the distribution and compositional variation of plant communities at the continental scale

P. 978-990Aim: To investigate whether (1) environmental predictors allow to delineate the distri- bution of discrete community types at the continental scale and (2) how data complete- ness influences model generalization in relation to the compositional variation of the modelled entities.S

Modelling the distribution and compositional variation of plant communities at the continental scale

Aim We investigate whether (1) environmental predictors allow to delineate the distribution of discrete community types at the continental scale and (2) how data completeness influences model generalization in relation to the compositional variation of the modelled entities. Location Europe. Methods We used comprehensive datasets of two community types of conservation concern in Europe: acidophilous beech forests and base‐rich fens. We computed community distribution models (CDMs) calibrated with environmental predictors to predict the occurrence of both community types, evaluating geographical transferability, interpolation and extrapolation under different scenarios of sampling bias. We used generalized dissimilarity modelling (GDM) to assess the role of geographical and environmental drivers in compositional variation within the predicted distributions. Results For the two community types, CDMs computed for the whole study area provided good performance when evaluated by random cross‐validation and external validation. Geographical transferability provided lower but relatively good performance, while model extrapolation performed poorly when compared with interpolation. Generalized dissimilarity modelling showed a predominant effect of geographical distance on compositional variation, complemented with the environmental predictors that also influenced habitat suitability. Main conclusions Correlative approaches typically used for modelling the distribution of individual species are also useful for delineating the potential area of occupancy of community types at the continental scale, when using consistent definitions of the modelled entity and high data completeness. The combination of CDMs with GDM further improves the understanding of diversity patterns of plant communities, providing spatially explicit information for mapping vegetation diversity and related habitat types at large scales

Modelling the distribution and compositional variation of plant communities at the continental scale

Aim We investigate whether (1) environmental predictors allow to delineate the distribution of discrete community types at the continental scale and (2) how data completeness influences model generalization in relation to the compositional variation of the modelled entities. Location Europe. Methods We used comprehensive datasets of two community types of conservation concern in Europe: acidophilous beech forests and base‐rich fens. We computed community distribution models (CDMs) calibrated with environmental predictors to predict the occurrence of both community types, evaluating geographical transferability, interpolation and extrapolation under different scenarios of sampling bias. We used generalized dissimilarity modelling (GDM) to assess the role of geographical and environmental drivers in compositional variation within the predicted distributions. Results For the two community types, CDMs computed for the whole study area provided good performance when evaluated by random cross‐validation and external validation. Geographical transferability provided lower but relatively good performance, while model extrapolation performed poorly when compared with interpolation. Generalized dissimilarity modelling showed a predominant effect of geographical distance on compositional variation, complemented with the environmental predictors that also influenced habitat suitability. Main conclusions Correlative approaches typically used for modelling the distribution of individual species are also useful for delineating the potential area of occupancy of community types at the continental scale, when using consistent definitions of the modelled entity and high data completeness. The combination of CDMs with GDM further improves the understanding of diversity patterns of plant communities, providing spatially explicit information for mapping vegetation diversity and related habitat types at large scales