a b s t r a c tThe economically important soapberry family (Sapindaceae) comprises about 1900 species mainly found in the tropical regions of the world, with only a few genera being restricted to temperate areas. The infrafamilial classification of the Sapindaceae and its relationships to the closely related Aceraceae and Hippocastanaceae -which have now been included in an expanded definition of Sapindaceae (i.e., subfamily Hippocastanoideae) -have been debated for decades. Here we present a phylogenetic analysis of Sapindaceae based on eight DNA sequence regions from the plastid and nuclear genomes and including 85 of the 141 genera defined within the family. Our study comprises 997 new sequences of Sapindaceae from 152 specimens. Despite presenting 18.6% of missing data our complete data set produced a topology fully congruent with the one obtained from a subset without missing data, but including fewer markers. The use of additional information therefore led to a consistent result in the relative position of clades and allowed the definition of a new phylogenetic hypothesis. Our results confirm a high level of paraphyly and polyphyly at the subfamilial and tribal levels and even contest the monophyletic status of several genera. Our study confirms that the Chinese monotypic genus Xanthoceras is sister to the rest of the family, in which subfamily Hippocastanoideae is sister to a clade comprising subfamilies Dodonaeoideae and Sapindoideae. On the basis of the strong support demonstrated in Sapindoideae, Dodonaeoideae and Hippocastanoideae as well as in 14 subclades, we propose and discuss informal groupings as basis for a new classification of Sapindaceae.

Aim Recently developed parametric methods in historical biogeography allow researchers to integrate temporal and palaeogeographical information into the reconstruction of biogeographical scenarios, thus overcoming a known bias of parsimony-based approaches. Here, we compare a parametric method, dispersalextinction-cladogenesis (DEC), against a parsimony-based method, dispersalvicariance analysis (DIVA), which does not incorporate branch lengths but accounts for phylogenetic uncertainty through a Bayesian empirical approach (Bayes-DIVA). We analyse the benefits and limitations of each method using the cosmopolitan plant family Sapindaceae as a case study.Location World-wide.Methods Phylogenetic relationships were estimated by Bayesian inference on a large dataset representing generic diversity within Sapindaceae. Lineage divergence times were estimated by penalized likelihood over a sample of trees from the posterior distribution of the phylogeny to account for dating uncertainty in biogeographical reconstructions. We compared biogeographical scenarios between Bayes-DIVA and two different DEC models: one with no geological constraints and another that employed a stratified palaeogeographical model in which dispersal rates were scaled according to area connectivity across four time slices, reflecting the changing continental configuration over the last 110 million years.Results Despite differences in the underlying biogeographical model, Bayes-DIVA and DEC inferred similar biogeographical scenarios. The main differences were: (1) in the timing of dispersal events -which in Bayes-DIVA sometimes conflicts with palaeogeographical information, and (2) in the lower frequency of terminal dispersal events inferred by DEC. Uncertainty in divergence time estimations influenced both the inference of ancestral ranges and the decisiveness with which an area can be assigned to a node.Main conclusions By considering lineage divergence times, the DEC method gives more accurate reconstructions that are in agreement with palaeogeographical evidence. In contrast, Bayes-DIVA showed the highest decisiveness in unequivocally reconstructing ancestral ranges, probably reflecting its ability to integrate phylogenetic uncertainty. Care should be taken in defining the palaeogeographical model in DEC because of the possibility of overestimating the frequency of extinction events, or of inferring ancestral ranges that are outside the extant species ranges, owing to dispersal constraints enforced by the model. The wide-spanning spatial and temporal model proposed here could prove useful for testing large-scale biogeographical patterns in plants.

Climatic history and ecology are considered the most important factors moulding the spatial pattern of genetic diversity. With the advent of molecular markers, speciesÕ historical fates have been widely explored. However, it has remained speculative what role ecological factors have played in shaping spatial genetic structures within species. With an unprecedented, dense large-scale sampling and genome-screening, we tested how ecological factors have influenced the spatial genetic structures in Alpine plants.Here, we show that species growing on similar substrate types, largely determined by the nature of bedrock, displayed highly congruent spatial genetic structures. As the heterogeneous and disjunctive distribution of bedrock types in the Alps, decisive for refugial survival during the ice ages, is temporally stable, concerted post-glacial migration routes emerged. Our multispecies study demonstrates the relevance of particular ecological factors in shaping genetic patterns, which should be considered when modelling species projective distributions under climate change scenarios.

The Convention on Biological Diversity (CBD) aims at the conservation of all three levels of biodiversity, that is, ecosystems, species and genes. Genetic diversity represents evolutionary potential and is important for ecosystem functioning. Unfortunately, genetic diversity in natural populations is hardly considered in conservation strategies because it is difficult to measure and has been hypothesised to co-vary with species richness. This means that species richness is taken as a surrogate of genetic diversity in conservation planning, though their relationship has not been properly evaluated. We tested whether the genetic and species levels of biodiversity co-vary, using a large-scale and multi-species approach. We chose the high-mountain flora of the Alps and the Carpathians as study systems and demonstrate that species richness and genetic diversity are not correlated. Species richness thus cannot act as a surrogate for genetic diversity. Our results have important consequences for implementing the CBD when designing conservation strategies.

Species range shifts in response to climate and land use change are commonly forecasted with species distribution models based on species occurrence or abundance data. Although appealing, these models ignore the genetic structure of species, and the fact that different populations might respond in different ways because of adaptation to their environment. Here, we introduced ancestry distribution models, that is, statistical models of the spatial distribution of ancestry proportions, for forecasting intra-specific changes based on genetic admixture instead of species occurrence data. Using multi-locus genotypes and extensive geographic coverage of distribution data across the European Alps, we applied this approach to 20 alpine plant species considering a global increase in temperature from 0.25 to 4 °C. We forecasted the magnitudes of displacement of contact zones between plant populations potentially adapted to warmer environments and other populations. While a global trend of movement in a north-east direction was predicted, the magnitude of displacement was species-specific. For a temperature increase of 2 °C, contact zones were predicted to move by 92 km on average (minimum of 5 km, maximum of 212 km) and by 188 km for an increase of 4 °C (minimum of 11 km, maximum of 393 km). Intra-specific turnover-measuring the extent of change in global population genetic structure-was generally found to be moderate for 2 °C of temperature warming. For 4 °C of warming, however, the models indicated substantial intra-specific turnover for ten species. These results illustrate that, in spite of unavoidable simplifications, ancestry distribution models open new perspectives to forecast population genetic changes within species and complement more traditional distribution-based approaches.