New mid-Cretaceous cryptic slime mold beetles and the early evolution of Sphindidae (Coleoptera: Cucujoidea)

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Abstract

Abstract The cryptic slime mold beetles, Sphindidae, are a moderately diverse cucujoid beetle family, whose members are obligately tied to slime molds throughout their life. The fossil record of sphindid beetles is sparse; stem-sphindids and crown-group members of uncertain systematic placement have been reported from Cretaceous ambers. Here we review the Mesozoic fossil record of Sphindidae and report a new sphindid genus and species, Trematosphindus newtoni gen. et sp. nov., from Albian/Cenomanian amber from northern Myanmar (ca. 99 Ma). Trematosphindus is set apart from all other sphindids by the presence of distinct lateral cavities on the anterior pronotal angles. Our phylogenetic analysis identifies Trematosphindus as an early-diverging genus within Sphindidae, sister to the remainder of the family except Protosphindus, or Protosphindus and Odontosphindus. The new fossils provide evidence that basal crown slime mold beetles begun to diversify by the mid-Cretaceous, providing a valuable calibration point for understanding timescale of sphindid co-evolution with slime molds.

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MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

Fredrik Ronquist, Maxim Teslenko, Paul van der Mark … (2012)

Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.

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TNT version 1.5, including a full implementation of phylogenetic morphometrics

Pablo Goloboff, Santiago A. Catalano (2016)

Version 1.5 of the computer program TNT completely integrates landmark data into phylogenetic analysis. Landmark data consist of coordinates (in two or three dimensions) for the terminal taxa; TNT reconstructs shapes for the internal nodes such that the difference between ancestor and descendant shapes for all tree branches sums up to a minimum; this sum is used as tree score. Landmark data can be analysed alone or in combination with standard characters; all the applicable commands and options in TNT can be used transparently after reading a landmark data set. The program continues implementing all the types of analyses in former versions, including discrete and continuous characters (which can now be read at any scale, and automatically rescaled by TNT). Using algorithms described in this paper, searches for landmark data can be made tens to hundreds of times faster than it was possible before (from T to 3T times faster, where T is the number of taxa), thus making phylogenetic analysis of landmarks feasible even on standard personal computers.