Biology subjects

David H Lunt

Publications and source records attributed to David H Lunt.

3 recordsLinked to original sources

bioRxiv2015

Nested metabarcode tagging: a robust tool for studying species interactions in ecology and evolution

Determining the host-parasitoid interactions and parasitism rates for invasive species entering novel environments is an important first step in assessing potential routes for biocontrol and integrated pest management. Conventional insect rearing techniques followed by taxonomic identification are widely used to obtain such data, but this can be time consuming and prone to biases. Here we present a Next Generation Sequencing approach for use in ecological studies which allows for individual level metadata tracking of large numbers of invertebrate samples through the use of hierarchically organised molecular identification tags. We demonstrate its utility using a sample data set examining both species identity and levels of parasitism in late larval stages of the Oak Processionary Moth (Thaumetopoea processionea - Linn. 1758), an invasive species recently established in the UK. Overall we find that there are two main species exploiting the late larval stages of Oak Processionary Moth in the UK with the main parasitoid (Carcelia iliaca-Ratzeburg, 1840) parasitising 45.7% of caterpillars, while a rare secondary parasitoid (Compsilura conccinata-Meigen, 1824) was also detected in 0.4% of caterpillars. Using this approach on all life stages of the Oak Processionary Moth may demonstrate additional parasitoid diversity. We discuss the wider potential of nested tagging DNA-metabarcoding for constructing large, highly-resolved species interaction networks.

Ecology↗

bioRxiv2015

Purifying selection and drift, not life history or RNAi, determine transposable element evolution

Transposable elements (TEs) are a major source of genome variation across the branches of life. Although TEs may occasionally play an adaptive role in their hosts genome, they are much more often deleterious, and purifying selection is thus an important factor controlling genomic TE loads. In contrast, life history and genomic characteristics such as mating system, parasitism, GC content, and RNAi pathways, have been suggested to account for the startling disparity of TE loads in different species. Previous studies of fungal, plant, and animal genomes have reported conflicting results regarding the direction in which these genomic features drive TE evolution. Many of these studies have had limited power because they studied taxonomically narrow systems, comparing only a limited number of phylogenetically independent contrasts, and did not address long term effects on TE evolution. Here we explicitly test the long term determinants of TE evolution by comparing 42 nematode genomes that span over 500 million years of diversification, and include numerous transitions between life history states and RNAi pathways. We have analysed the reconstructed TE loads of ancestors through the Nematoda phylogeny to account for correlation with GC content and transitions in TE evolutionary models. We also analysed the effect of transitions in life history characteristics and RNAi using ANOVA of phylogenetically independent contrasts. We show that purifying selection against TEs is the dominant force throughout the evolutionary history of Nematoda, as indicated by reconstructed ancestral TE loads, and that strong stochastic Ornstein-Uhlenbeck processes are the underlying models which best explain TE diversification among extant species. In contrast we found no evidence that life history or RNAi variations have a significant influence upon genomic TE load across extended periods of evolutionary history. We suggest that these are largely inconsequential to the large differences in TE content observed between genomes and only by these large-scale comparisons can we distinguish long term and persistent effects from transient effects or misleading random changes.

Evolutionary Biology↗

bioRxiv2015

ReproPhylo: An Environment for Reproducible Phylogenomics

The reproducibility of experiments is key to the scientific process, and particularly necessary for accurate reporting of analyses in data-rich fields such as phylogenomics. We present ReproPhylo, a phylogenomic analysis environment developed to ensure experimental reproducibility, to facilitate the handling of large-scale data, and to assist methodological experimentation. Reproducibility, and instantaneous repeatability, is built in to the ReproPhylo system, and does not require user intervention or configuration because it stores the experimental workflow as a single, serialized Python object containing explicit provenance and environment information. This single file approach ensures the persistence of provenance across iterations of the analysis, with changes automatically managed by the version control program Git. ReproPhylo produces an extensive human-readable report, and generates a comprehensive experimental archive file, both of which are suitable for submission with publications. The system facilitates thorough experimental exploration of both parameters and data. ReproPhylo is a platform independent CC0 python module, and is easily installed as a Docker image, with an Jupyter GUI, or as a slimmer version in a Galaxy distribution.

Bioinformatics↗