Time flies… in the blink of an eye! And even more so in science. The molecular lab work we were used to two decades ago seems like ancient history to today’s PhD students. The speed of change in sequencing technology is so overwhelming that imagination usually fails to foresee how our daily work will be in 10 years’ time. But in the field of biodiversity assessment, we have very good clues. Next Generation Sequencing is quickly becoming our workhorse for ambitious projects of species and genetic inventories.
One by One Approach to Studying Biodiversity
For decades, most initiatives measured biodiversity in the same way: collect a sample of many individuals in the field, sort the specimens, identify them to a Linnaean species one at a time (if there was a good taxonomist in the group which, unfortunately, it is kind of lucky these days!), and count them. Or, if identification was based on molecular data, the specimen was subject to DNA extraction, to sequence one (or several) short DNA markers. This involved countless hours of work that could be saved if, instead of inventorying biodiversity specimen-by-specimen, we followed a sample-by-sample approach. To do this now, we just have to make a “biodiversity soup”.
Biodiversity assessment based on morphological identification and/or Sanger sequencing (“The one-by-one approach”)
“In some years, chum salmon are frequently the bycatch of pollock fishermen” in the Bering Sea, Garvin explained. “Genetically, chum salmon that originate in Western Alaska tend to look very similar. This makes it difficult for stakeholders because management and conservation efforts to address this bycatch can differ among these regions, but the ability to identify them with genetics is not possible.” Continue reading →
DNA dietary analysis is a non-invasive tool used to identify the food consumed by vertebrates. The method relies on identifying prey DNA in the target animals’ scats. It’s especially useful for marine animals such as seals and seabirds as it is difficult to watch their feeding events.
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 →