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Calceolaria (Calceolariaceae) is an emblematic and diverse genus in the Americas. Despite being one of the most easily recognized genera in the region and a system with great potential to improve our understanding of different drivers of species diversification in the Andes, its intrageneric evolutionary relationships are still poorly understood. Responding to the need for additional molecular markers to resolve the phylogenetic relationships of the group, we perform plastome analyses and resolve the backbone of the genus. Specifically, using low-coverage genomes for 14 species, we assembled plastomes, estimated and dated phylogenetic hypotheses and evaluated evolutionary trends in the group. Our approach allowed us to resolve the backbone of the genus, identify two main clades and estimate a timing of diversification contemporaneous to major climatic and orogenic events. Our biogeographic reconstructions suggest an independent colonization of the whole range of the genus by both clades. Finally, our evaluations of floral morphology reveal future avenues for investigating the relationship between the pollination biology and diversification of the group.

The conservation status of most plant species is currently unknown, despite the fundamental role of plants in ecosystem health. To facilitate the costly process of conservation assessment, we developed a predictive protocol using a machine-learning approach to predict conservation status of over 150,000 land plant species. Our study uses open-source geographic, environmental, and morphological trait data, making this the largest assessment of conservation risk to date and the only global assessment for plants. Our results indicate that a large number of unassessed species are likely at risk and identify several geographic regions with the highest need of conservation efforts, many of which are not currently recognized as regions of global concern. By providing conservation-relevant predictions at multiple spatial and taxonomic scales, predictive frameworks such as the one developed here fill a pressing need for biodiversity science.

The history of the currently disjunct temperate rainforests of the Pacific Northwest of North America has shaped the evolution and diversity of endemics. This study focuses on how geological and climatic perturbations have driven speciation in the area by isolating lineages. We investigated the phylogenetic relationships and historical biogeography of the endemic jumping slugs (genus Hemphillia) using a multi-locus phylogeny. We evaluated the spatial distribution and divergence times of major lineages, generated ancestral area probabilities and inferred the biogeographical history of the genus. Our study revealed eight genetic lineages that formed three clades: one clade consisting of two Coast/Cascade lineages, and two reciprocally monophyletic clades that each contain a Coast/Cascade and two Rocky Mountains taxa. The results of the biogeographical analysis suggest that the ancestral range of the genus occupied Coast/Cascade habitats and then spread across into Northern Rocky Mountain interior habitats with subsequent fragmentations isolating coastal and inland lineages. Finally, there have been more recent speciation events among three lineage pairs that have shaped shallow structures of all clades. We add to our knowledge of the biogeographical history of the region in that we discovered diversification and speciation events that have occurred in ways more complex than previously thought.

Predictive phylogeography seeks to aggregate genetic, environmental and taxonomic data from multiple species in order to make predictions about unsampled taxa using machine‐learning techniques such as Random Forests. To date, organismal trait data have infrequently been incorporated into predictive frameworks due to difficulties inherent to the scoring of trait data across a taxonomically broad set of taxa. We refine predictive frameworks from two North American systems, the inland temperate rainforests of the Pacific Northwest and the Southwestern Arid Lands (SWAL), by incorporating a number of organismal trait variables. Our results indicate that incorporating life history traits as predictor variables improves the performance of the supervised machine‐learning approach to predictive phylogeography, especially for the SWAL system, in which predictions made from only taxonomic and climate variables meets only moderate success. In particular, traits related to reproduction (e.g., reproductive mode; clutch size) and trophic level appear to be particularly informative to the predictive framework. Predictive frameworks offer an important mechanism for integration of organismal trait, environmental data, and genetic data in phylogeographic studies.