The tropical Asian taxa of the species-rich genus Solanum (Solanaceae) have been less well studied than their highly diverse New World relatives. Most of these tropical Asian species, including the cultivated brinjal eggplant/aubergine and its wild progenitor, are part of the largest monophyletic Solanum lineage, the 'spiny solanums' (subgenus Leptostemonum or the Leptostemonum clade). Here we present the first phylogenetic analysis of spiny solanums that includes broad sampling of the tropical Asian species, with 42 of the 56 currently recognized species represented. Two nuclear and three plastid regions [internal transcribed spacer (ITS), waxy, ndhF-rpL32, trnS-trnG and trnT-trnF] were amplified and used to reconstruct phylogenetic relationships using maximum likelihood and Bayesian methods. Our analyses show that Old World spiny solanums do not resolve in a single clade, but are part of three unrelated lineages, suggesting at least three independent introductions from the New World. We identify and describe several monophyletic groups in Old World solanums that have not been previously recognized. Some of these lineages are coherent in terms of morphology and geography, whereas others show considerable morphological variation and enigmatic distribution patterns. Tropical Asia occupies a key position in the biogeography of Old World spiny solanums, with tropical Asian taxa resolved as the closest relatives of diverse groups of species from Australia and Africa.

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Despite the paramount role of plant diversity for ecosystem functioning, biogeochemical cycles, and human welfare, knowledge of its global distribution is still incomplete, hampering basic research and biodiversity conservation.Here, we used machine learning (random forests, extreme gradient boosting, and neural networks) and conventional statistical methods (generalized linear models and generalized additive models) to test environment-related hypotheses of broad-scale vascular plant diversity gradients and to model and predict species richness and phylogenetic richness worldwide. To this end, we used 830 regional plant inventories including c. 300 000 species and predictors of past and present environmental conditions.Machine learning showed a superior performance, explaining up to 80.9% of species richness and 83.3% of phylogenetic richness, illustrating the great potential of such techniques for disentangling complex and interacting associations between the environment and plant diversity. Current climate and environmental heterogeneity emerged as the primary drivers, while past environmental conditions left only small but detectable imprints on plant diversity.Finally, we combined predictions from multiple modeling techniques (ensemble predictions) to reveal global patterns and centers of plant diversity at multiple resolutions down to 7774 km 2 . Our predictive maps provide accurate estimates of global plant diversity available at grain sizes relevant for conservation and macroecology.

Despite an existing India-wide inventory of alien plant species, an inventory documenting the occurrence of naturalized alien plant species in each of the Indian states (including union territories) was not available yet. We compiled from the literature a list of naturalized alien vascular plant species with data on their occurrence in 33 Indian states, and related the richness of naturalized species per state to climate, socioeconomic parameters and human influence. In total, we report 471 naturalized species in India, which

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