Tip dating supports novel resolutions of controversial relationships among early mammals

The estimation of the timing of major divergences in early mammal evolution is challenging due to conflicting interpretations of key fossil taxa. One contentious group is Haramiyida, the earliest members of which are from the Late Triassic. Many phylogenetic analyses have placed haramiyidans in a clade with multituberculates within crown Mammalia, thus extending the minimum divergence date for the crown group deep into the Triassic. A second taxon of interest is the eutherian Juramaia from the Middle-Late Jurassic Yanliao Biota, which is morphologically very similar to eutherians from the Early Cretaceous Jehol Biota and implies a very early origin for therian mammals. Here we apply Bayesian tip-dating phylogenetic methods to investigate these issues. Tip dating firmly rejects a monophyletic Allotheria (multituberculates and haramiyidans), which are split into three separate clades, a result not found in any previous analysis. Most notably, the Late Triassic Haramiyavia and Thomasia are separate from the Middle Jurassic euharamiyidans. We also test whether the Middle–Late Jurassic age of Juramaia is ‘expected’ given its known morphology by assigning an age prior without hard bounds. Strikingly, this analysis supports an Early Cretaceous age for Juramaia, but similar analyses on twelve other mammaliaforms from the Yanliao biota return the correct, Jurassic age. Our results show that analyses incorporating stratigraphic data can produce results very different from other methods. Early mammal evolution may have involved multiple instances of convergent morphological evolution (e.g., in the dentition), and tip dating may be a method uniquely suitable to recognising this due to the incorporation of stratigraphic data. Our results also confirm that Juramaia is anomalous in exhibiting a much more derived morphology than expected given its age, which in turn implies very high rates of evolution at the base of therian mammals

Kindia (Pavetteae, Rubiaceae), a new cliff-dwelling genus with chemically profiled colleter exudate from Mt Gangan, Republic of Guinea

A new genus Kindia (Pavetteae, Rubiaceae) is described with a single species, Kindia gangan, based on collections made in 2016 during botanical exploration of Mt Gangan, Kindia, Republic of Guinea in West Africa. The Mt Gangan area is known for its many endemic species including the only native non-neotropical Bromeliaceae Pitcairnia feliciana. Kindia is the fourth endemic vascular plant genus to be described from Guinea. Based on chloroplast sequence data, the genus is part of Clade II of tribe Pavetteae. In this clade, it is sister to Leptactina sensu lato (including Coleactina and Dictyandra). K. gangan is distinguished from Leptactina s.l. by the combination of the following characters: its epilithic habit; several-flowered axillary inflorescences; distinct calyx tube as long as the lobes; a infundibular-campanulate corolla tube with narrow proximal section widening abruptly to the broad distal section; presence of a dense hair band near base of the corolla tube; anthers and style deeply included, reaching about mid-height of the corolla tube; anthers lacking connective appendages and with sub-basal insertion; pollen type 1; pollen presenter (style head) winged and glabrous (smooth and usually hairy in Leptactina); orange colleters producing a vivid red exudate, which encircle the hypanthium, and occur inside the calyx and stipules. Kindia is a subshrub that appears restricted to bare, vertical rock faces of sandstone. Fruit dispersal and pollination by bats is postulated. Here, it is assessed as Endangered EN D1 using the 2012 IUCN standard. High resolution LC-MS/MS analysis revealed over 40 triterpenoid compounds in the colleter exudate, including those assigned to the cycloartane class. Triterpenoids are of interest for their diverse chemical structures, varied biological activities, and potential therapeutic value

Inferring phylogenetic networks with maximum pseudolikelihood under incomplete lineage sorting

Phylogenetic networks are necessary to represent the tree of life expanded by edges to represent events such as horizontal gene transfers, hybridizations or gene flow. Not all species follow the paradigm of vertical inheritance of their genetic material. While a great deal of research has flourished into the inference of phylogenetic trees, statistical methods to infer phylogenetic networks are still limited and under development. The main disadvantage of existing methods is a lack of scalability. Here, we present a statistical method to infer phylogenetic networks from multi-locus genetic data in a pseudolikelihood framework. Our model accounts for incomplete lineage sorting through the coalescent model, and for horizontal inheritance of genes through reticulation nodes in the network. Computation of the pseudolikelihood is fast and simple, and it avoids the burdensome calculation of the full likelihood which can be intractable with many species. Moreover, estimation at the quartet-level has the added computational benefit that it is easily parallelizable. Simulation studies comparing our method to a full likelihood approach show that our pseudolikelihood approach is much faster without compromising accuracy. We applied our method to reconstruct the evolutionary relationships among swordtails and platyfishes ($Xiphophorus$: Poeciliidae), which is characterized by widespread hybridizations

Overcoming the data crisis in biodiversity conservation

How can we track population trends when monitoring data are sparse? Population declines can go undetected, despite ongoing threats. For example, only one of every 200 harvested species are monitored. This gap leads to uncertainty about the seriousness of declines and hampers effective conservation. Collecting more data is important, but we can also make better use of existing information. Prior knowledge of physiology, life history, and community ecology can be used to inform population models. Additionally, in multispecies models, information can be shared among taxa based on phylogenetic, spatial, or temporal proximity. By exploiting generalities across species that share evolutionary or ecological characteristics within Bayesian hierarchical models, we can fill crucial gaps in the assessment of species’ status with unparalleled quantitative rigor