Repeatedly Northwards and Upwards: Southern African Grasslands Fuel the Colonization of the African Sky Islands in Helichrysum (Compositae).

The Afromontane and Afroalpine areas constitute some of the main biodiversity hotspots of Africa. They are particularly rich in plant endemics, but the biogeographic origins and evolutionary processes leading to this outstanding diversity are poorly understood. We performed phylogenomic and biogeographic analyses of one of the most species-rich plant genera in these mountains, Helichrysum (Compositae-Gnaphalieae). Most previous studies have focused on Afroalpine elements of Eurasian origin, and the southern African origin of Helichrysum provides an interesting counterexample. We obtained a comprehensive nuclear dataset from 304 species (≈50% of the genus) using target-enrichment with the Compositae1061 probe set. Summary-coalescent and concatenation approaches combined with paralog recovery yielded congruent, well-resolved phylogenies. Ancestral range estimations revealed that Helichrysum originated in arid southern Africa, whereas the southern African grasslands were the source of most lineages that dispersed within and outside Africa. Colonization of the tropical Afromontane and Afroalpine areas occurred repeatedly throughout the Miocene-Pliocene. This timing coincides with mountain uplift and the onset of glacial cycles, which together may have facilitated both speciation and intermountain gene flow, contributing to the evolution of the Afroalpine flora.

SSR markers for Filago subg. Filago (Gnaphalieae: Asteraceae) and cross-amplification in three other subgenera.

PREMISE OF THE STUDY: Microsatellite primers were developed for the first time in the genus Filago (Gnaphalieae: Asteraceae). These markers will facilitate low-scale phylogenetic, phylogeographic, and population genetic studies within the genus Filago. METHODS AND RESULTS: Ten pairs of polymorphic microsatellite primers (as well as five pairs of monomorphic primers) were identified and optimized on two species of Filago (F. gaditana and F. carpetana) using a microsatellite-enrichment library method and 454 GS-FLX technique. The polymorphic primers amplified tri- to hexanucleotide repeats and showed one to six alleles per locus for both species. Transferability was performed in 29 samples corresponding to nine representative species of Filago. CONCLUSIONS: The results indicate the utility of the newly developed markers, which will be useful to delve into the phylogenetic relationships among the taxa within Filago. These microsatellites will enable studies of phylogeographic, reproductive, and genetic variation.

Divide and conquer! Data-mining tools and sequential multivariate analysis to search for diagnostic morphological characters within a plant polyploid complex (Veronica subsect. Pentasepalae, Plantaginaceae).

This study exhaustively explores leaf features seeking diagnostic characters to aid the classification (assigning cases to groups, i.e. populations to taxa) in a polyploid plant-species complex. A challenging case study was selected: Veronica subsection Pentasepalae, a taxonomically intricate group. The "divide and conquer" approach was implemented-that is, a difficult primary dataset was split into more manageable subsets. Three techniques were explored: two data-mining tools (artificial neural networks and decision trees) and one unsupervised discriminant analysis. However, only the decision trees and discriminant analysis were finally used to select diagnostic traits. A previously established classification hypothesis based on other data sources was used as a starting point. A guided discriminant analysis (i.e. involving manual character selection) was used to produce a grouping scheme fitting this hypothesis so that it could be taken as a reference. Sequential unsupervised multivariate analysis enabled the recognition of all species and infraspecific taxa; however, a suboptimal classification rate was achieved. Decision trees resulted in better classification rates than unsupervised multivariate analysis, but three complete taxa were misidentified (not present in terminal nodes). The variable selection led to a different grouping scheme in the case of decision trees. The resulting groups displayed low misclassification rates when analyzed using artificial neural networks. The decision trees as well as the discriminant analysis are recommended in the search of diagnostic characters. Due to the high sensitivity that artificial neural networks have to the combination of input/output layers, they are proposed as evaluation tools for morphometric studies. The "divide and conquer" principle is a promising strategy, providing success in the present case study.