A species-level phylogeny of all extant and late Quaternary extinct mammals using a novel heuristic-hierarchical Bayesian approach

Abstract

Across large clades, two problems are generally encountered in the estimation of species-level phylogenies: (a) the number of taxa involved is generally so high that computation-intensive approaches cannot readily be utilized and (b) even for clades that have received intense study (e.g., mammals), attention has been centered on relatively few selected species, and most taxa must therefore be positioned on the basis of very limited genetic data. Here, we describe a new heuristic-hierarchical Bayesian approach and use it to construct a species-level phylogeny for all extant and late Quaternary extinct mammals. In this approach, species with large quantities of genetic data are placed nearly freely in the mammalian phylogeny according to these data, whereas the placement of species with lower quantities of data is performed with steadily stricter restrictions for decreasing data quantities. The advantages of the proposed method include (a) an improved ability to incorporate phylogenetic uncertainty in downstream analyses based on the resulting phylogeny, (b) a reduced potential for long-branch attraction or other types of errors that place low-data taxa far from their true position, while maintaining minimal restrictions for better-studied taxa, and (c) likely improved placement of low-data taxa due to the use of closer outgroups.

Infomap Bioregions: Interactive Mapping of Biogeographical Regions from Species Distributions

“…For terrestrial mammals, we downloaded a set of 1000 species-level phylogenies of all mammals from Faurby and Svenning (2015) and calculated the maximum clade credibility tree using TreeAnnotator in the package BEAST v.1.8.2 (Drummond and Rambaut 2007). We were able to match 4426 species between the phylogeny and the georeferenced data set.…”

Go big or go fish: morphological specializations in carnivorous bats

“…Interestingly, piscivorous bats emerge as a guild with unique and extreme cranial morphologies. Separated by at least 55 Myr of evolution [41], the two piscivorous species in the dataset (Noctilio leporinus and Myotis vivesi) have converged in cranial morphologies characterized by a short, broad and tall or dorsally projected rostrum, and broad zygomatic arches. This morphology results in a shorter out lever that is suited for producing high bite forces at low gapes [71,72], and potentially confers more resistance during cyclical molar chewing [73].…”

“…To test for differences in size across diets, we conducted phylogenetic ANOVAs (10 000 iterations) with a pruned version of the latest species-level mammal phylogeny [41]. Centroid sizes of the cranium and mandible, and log 10 body mass [40] were the response variables, respectively, and dietary categories (above) were the factors.…”

Strong influence of palaeoclimate on the structure of modern African mammal communities

“…The mammalian phylogeny of Bininda-Emonds et al [64] was used for all phylogenetic analyses, as this phylogeny contains all species in our dataset and has been widely used in previous research [2,5,65,66]. However, we also calculated NRI and NTI from the newly available Faurby & Svenning [67] species-level phylogeny of all extant mammals. The NRI and NTI values calculated from the Faurby & Svenning [67] phylogeny were in almost all cases highly correlated with those using the Bininda-Emonds et al [64] We used four metrics outlined by Kraft et al [16] and Kraft & Ackerly [1] to characterize the functional trait structure of communities: range, variance, the standard deviation of nearest neighbour distance divided by the overall trait range (SDNNr), and the standard deviation of neighbour distance divided by the overall trait range (SDNDr).…”

“…However, we also calculated NRI and NTI from the newly available Faurby & Svenning [67] species-level phylogeny of all extant mammals. The NRI and NTI values calculated from the Faurby & Svenning [67] phylogeny were in almost all cases highly correlated with those using the Bininda-Emonds et al [64] We used four metrics outlined by Kraft et al [16] and Kraft & Ackerly [1] to characterize the functional trait structure of communities: range, variance, the standard deviation of nearest neighbour distance divided by the overall trait range (SDNNr), and the standard deviation of neighbour distance divided by the overall trait range (SDNDr). Two of these metrics (range, variance) are sensitive to habitat filtering, while the other two metrics (SDNNr, SDNDr) detect even spacing-a common pattern resulting from interspecific competition and niche partitioning [1].…”