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Datasets of hundreds or thousands of SNPs (Single Nucleotide Polymorphisms) from multiple individuals per species are increasingly used to study population structure, species delimitation and shallow phylogenetics. The principal software tool to infer species or population trees from SNP data is currently the BEAST template SNAPP which uses a Bayesian coalescent analysis. However, it is computationally extremely demanding and tolerates only small amounts of missing data. We used simulated and empirical SNPs from plants (Australian Craspedia , Asteraceae, and Pelargonium , Geraniaceae) to compare species trees produced (1) by SNAPP, (2) using SVD quartets, and (3) using Bayesian and parsimony analysis with several different approaches to summarising data from multiple samples into one set of traits per species. Our aims were to explore the impact of tree topology and missing data on the results, and to test which data summarising and analyses approaches would best approximate the results obtained from SNAPP for empirical data. SVD quartets retrieved the correct topology from simulated data, as did SNAPP except in the case of a very unbalanced phylogeny. Both methods failed to retrieve the correct topology when large amounts of data were missing. Bayesian analysis of species level summary data scoring the two alleles of each SNP as independent characters and parsimony analysis of data scoring each SNP as one character produced trees with branch length distributions closest to the true trees on which SNPs were simulated. For empirical data, Bayesian inference and Dollo parsimony analysis of data scored allele-wise produced phylogenies most congruent with the results of SNAPP. In the case of study groups divergent enough for missing data to be phylogenetically informative (because of additional mutations preventing amplification of genomic fragments or bioinformatic establishment of homology), scoring of SNP data as a presence/absence matrix irrespective of allele content might be an additional option. As this depends on sampling across species being reasonably even and a random distribution of non-informative instances of missing data, however, further exploration of this approach is needed. Properly chosen data summary approaches to inferring species trees from SNP data may represent a potential alternative to currently available individual-level coalescent analyses especially for quick data exploration and when dealing with computationally demanding or patchy datasets. [ABSTRACT FROM AUTHOR]

Abstract: The type section of Primula (Primulaceae), here considered to include seven species, is phylogenetically quite isolated in its genus. Although its species are popular ornamentals, traditional medicinal plants and model organisms for the study of heterostyly, the section has not yet been studied from a phylogenetic or evolutionary perspective. Using phylogenetic analysis of nuclear ITS and plastid data from all species and subspecies, we find that widespread Primula elatior is genetically heterogeneous and non-monophyletic to most if not all of the other ingroup taxa. The Genealogical Sorting Index (GSI) indicates that the assumption of all currently accepted species being independent lineages is consistent with the data. It is possible that P. elatior in its current circumscription may represents the disjointed remnant of an ancestral species from which the other recognized species diverged. However, based on available data, the alternative possibility of introgression explaining the non-monophyly of this species cannot be excluded. Species trees show P. elatior and P. veris as sister species. Primula vulgaris and P. juliae are closely related, while, in contrast to previous assumptions, P. renifolia does not appear to be a close relative of P. megaseifolia. With the section’s isolation from the rest of the genus and very short internal branches, our dataset also presents a case study of the confounding effects of different branch length priors on the Bayesian estimation of resulting branch length estimates. Experimental runs using different priors confirm the problem of resulting estimates varying by orders of magnitude, while topology and relative branch lengths seem unaffected. [Copyright &y& Elsevier]

• Introduces a contemporary molecular analytics workflow centered on evolutionary relationships into biocontrol risk analysis. • Recommends the use of patristic (phylogenetic) distance over degrees of separation. • Employed target sequence capture for tribal-level phylogenomic analysis on flaxleaf fleabane case study. A concern in weed biological control research is the potential for candidate biocontrol agents to impact not only the target weed but also native or economically important flora. The degree of evolutionary relatedness between the target weed species and a non-target species is a key predictor of the susceptibility of the non-target to the biocontrol agent. To manage this risk, biocontrol practitioners need to understand the phylogenetic position of the host weed relative to non-target plant species. However, comprehensively sampled phylogenetic trees are often unavailable, with incomplete information scattered across multiple publications. Further, older published phylogenies based on Sanger sequence data often lack branch resolution and support, which increases uncertainty in biocontrol decision making. Decreasing sequencing cost and technological advances have led to phylogenomic approaches being more widely used to understand evolutionary relationships between species. For example, target capture sequencing methods using bait kits such as Angiosperms353 enable cost-effective and timely phylogenomic-level analysis of flowering plant groups at different scales. Here, we introduce a workflow to embed a comprehensive understanding of evolutionary relationships into the efficient development of host test lists in weed biological control. We demonstrate the effectiveness of the workflow through a case study on the major crop weed flaxleaf fleabane (Erigeron bonariensis). Phylogenomic analysis was conducted on 280 species of the tribe Astereae (family Asteraceae) occurring in Australia and New Zealand, clarifying relationships between the target species and related clades of native and non-native Astereae. We consider the phylogenetic tree in the context of a previously proposed host test list and discuss taxonomic implications, highlighting avenues of future molecular-based work to uncover the origin of Australian fleabanes. This study provides a workflow and demonstrates the practical application of target sequence capture for phylogenomic inference to support risk analysis and decision making in classical weed biological control. [ABSTRACT FROM AUTHOR]