Baobabs (Adansonia) are a cohesive group of tropical trees with a disjunct distribution in Australia, Madagascar, and continental Africa, and diverse flowers associated with two pollination modes. We used custom-targeted sequence capture in conjunction with new and existing phylogenetic comparative methods to explore the evolution of floral traits and pollination systems while allowing for reticulate evolution. Our analyses suggest that relationships in Adansonia are confounded by reticulation, with network inference methods supporting at least one reticulation event. The best supported hypothesis involves introgression between Adansonia rubrostipa and core Longitubae, both of which are hawkmoth pollinated with yellow/red flowers, but there is also some support for introgression between the African lineage and Malagasy Brevitubae, which are both mammal-pollinated with white flowers. New comparative methods for phylogenetic networks were developed that allow maximum-likelihood inference of ancestral states and were applied to study the apparent homoplasy in floral biology and pollination mode seen in Adansonia. This analysis supports a role for introgressive hybridization in morphological evolution even in a clade with highly divergent and geographically widespread species. Our new comparative methods for discrete traits on species networks are implemented in the software PhyloNetworks. [Comparative methods; Hyb-Seq; introgression; network inference; population trees; reticulate evolution; species tree inference; targeted sequence capture.]

PREMISE: Measuring plant productivity is critical to understanding complex community interactions. Many traditional methods for estimating productivity, such as direct measurements of biomass and cover, are resource intensive, and remote sensing techniques are emerging as viable alternatives.METHODS: We explore drone‐based remote sensing tools to estimate productivity in a tallgrass prairie restoration experiment and evaluate their ability to predict direct measures of productivity. We apply these various productivity measures to trace the evolution of plant productivity and the traits underlying it.RESULTS: The correlation between remote sensing data and direct measurements of productivity varies depending on vegetation diversity, but the volume of vegetation estimated from drone‐based photogrammetry is among the best predictors of biomass and cover regardless of community composition. The commonly used normalized difference vegetation index (NDVI) is a less accurate predictor of biomass and cover than other equally accessible vegetation indices. We found that the traits most strongly correlated with productivity have lower phylogenetic signal, reflecting the fact that high productivity is convergent across the phylogeny of prairie species. This history of trait convergence connects phylogenetic diversity to plant community assembly and succession.DISCUSSION: Our study demonstrates (1) the importance of considering phylogenetic diversity when setting management goals in a threatened North American grassland ecosystem and (2) the utility of remote sensing as a complement to ground measurements of grassland productivity for both applied and fundamental questions.

Phylogenetic diversity (PD), the evolutionary history of the organisms comprising a community, is increasingly recognized as an important driver of ecosystem function. However, biodiversity–ecosystem function experiments have rarely included PD as an a priori treatment. Thus, PD's effects in existing experiments are often confounded by covarying differences in species richness and functional trait diversity (FD). Here we report an experimental demonstration of strong PD effects on grassland primary productivity that are independent of FD, which was separately manipulated, and species richness, which was planted uniformly high to mimic diverse natural grasslands. Partitioning diversity effects demonstrated that higher PD increased complementarity (niche partitioning and/or facilitation) but lowered selection effects (probability of sampling highly productive species). Specifically, for every 5% increase in PD, complementarity increased by 26% on average (±8% SE), while selection effects decreased more modestly (8 ± 16%). PD also shaped productivity through clade‐level effects on functional traits, that is, trait values associated with particular plant families. This clade effect was most pronounced in the Asteraceae (sunflower family), which, in tallgrass prairies, generally comprises tall, high‐biomass species with low phylogenetic distinctiveness. FD also reduced selection effects but did not alter complementarity. Our results show that PD, independent of richness and FD, mediates ecosystem function through contrasting effects on complementarity and selection. This adds to growing evidence that consideration of phylogenetic dimensions of biodiversity can advance ecological understanding and inform conservation and restoration.

Quantifying spatial species richness is useful to describe biodiversity patterns across broad geographic areas, especially in large, poorly known plant groups. We explore patterns and predictors of species richness across Africa in one such group; the paleotropical genus Grewia L. (Malvaceae). Grewia species richness was quantified by extracting herbarium records from GBIF and Tropicos and creating geographic grids at varying spatial scales. We assessed predictors of species richness using spatial regression models with 30 environmental variables. We explored species co-occurrence in Madagascar at finer resolutions using Schoener's index, and compared species’ range sizes and IUCN status among ecoregions. Lastly, we derived a trait matrix for a subset of species found in Madagascar to characterize morphological diversity across space. Grewia species occur in 50 countries in Africa, with the highest number of species in Madagascar (93, with 80 species endemic). Species richness is highest in Madagascar, with up to 23 Grewia species in a grid cell, followed by coastal Tanzania/Kenya (up to 13 species), and northern South Africa and central Angola (11 species each). Across Africa, higher species richness was predicted by variables related to aridity. In Madagascar, a greater range in environmental variables best predicted species richness, consistent with geographic grid cells of highest species richness occurring near biome/ecoregion transitions. In Madagascar we also observe increasing dissimilarity in species composition with increasing geographic distance. The spatial patterns and underlying environmental predictors that we uncover in Grewia represent an important step in our understanding of plant distribution and diversity patterns across Africa. Madagascar boasts nearly twice the Grewia species richness, compared to the second most species-rich country in Africa, which might be explained by complex topography and environmental conditions across small spatial scales.