This month’s issue contains two Applications article and one Open Access article, all of which are freely available.
– LEA: This R package enables users to run ecological association studies from the R command line. It can perform analyses of population structure and genome scans for adaptive alleles from large genomic data sets. The package derives advantages from R programming functionalities to adjust significance values for multiple testing issues and to visualize results.
–PIPITS: An open-source stand-alone suite of software for automated processing of Illumina MiSeq sequences for fungal community analysis. PIPITS exploits a number of state of the art applications to process paired-end reads from quality filtering to producing OTU abundance tables.
Giovanni Strona and Joseph Veech provide this month’s Open Access article. Many studies have focused on nestedness, a pattern reflecting the tendency of network nodes to share interaction partners, as a method of measuring the structure of ecological networks. In ‘A new measure of ecological network structure based on node overlap and segregation‘ the authors introduce a new statistical procedure to measure both this kind of structure and the opposite one (i.e. species’ tendency against sharing interacting partners).
It may sound counter-intuitive, but crushing up bees into a ‘DNA soup’ could help conservationists understand and even reverse their decline – according to University of East Anglia scientists.
Research published today in the journal Methods in Ecology and Evolution shows that collecting wild bees, extracting their DNA, and directly reading the DNA of the resultant ‘soup’ could finally make large-scale bee monitoring programmes feasible.
This would allow conservationists to detect where and when bee species are being lost, and importantly, whether conservation interventions are working.
The UK’s National Pollinator Strategy outlines plans for a large-scale bee monitoring programme. Traditional monitoring involves pinning individual bees and identifying them under a microscope. But the number of bees needed to track populations reliably over the whole country makes traditional methods infeasible.
This new research shows how the process could become quicker, cheaper and more accurate. Continue reading →