Open Access

Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species’ threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project – and avert – future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups – including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems – www.predicts.org.uk). We make site-level summary data available alongside this article. The full database will be publicly available in 2015.

Biodiversity data are the foundation for conservation and managemet and taxonomy provides the reference system, skills and tools used to identify organisms. Species level data such as species richness, composition and diversity are common metrics. However, species level identification of organisms tends to be neglected within ecological work, especially within monitoring programmes, but also in conservation biology (Giangrande, 2003). This is because collection of species level data is time consuming, with identification of species-specific characteristics traditionally involving lengthy examination of samples using microscopy. In addition it is costly and species level data is almost impossible to collect if the taxa involved are species rich and difficult to identify (Báldi 1999). Other reasons why species level identification is neglected include the fact that sample collection can damage organisms, so diagnostic morphological features are lost, or that individuals may be in a life history stage or of a sex that does not have diagnostic morphological characteristics. Furthermore, the numbers of available expert taxonomists needed for species identification are in decline and have been for several decades. Species identification using molecular taxonomy where DNA is used as a marker is championed as a tool for resolving a range of morphological problems, such as the association of all life history stages, correlating male and female specimens to the same species and identifying partial specimens. Traditional taxonomy is built around morphological variations between species, with systematic inferences based upon shared physical characters. In molecular taxonomy on the other hand, proteins and genes are used to determine evolutionary relationships. ’DNA barcoding’ aims to provide an efficient method for species-level identification and it is thought that it will provide a powerful tool for taxonomic and biodiversity research (Hajibabaei et al. 2007). Cited strengths of a molecular based approach to species identification include the potential universality and objective nature of DNA data as taxonomic information, the usefulness of molecular data in animal groups characterized by morphological cryptic characters and the use of DNA sequence information to determine otherwise ‘unidentifiable’ biological material (such as incomplete specimens or immature specimens). Its aim is to increase the speed, precision and efficiency of field studies involving diverse and difficult to identify taxa and it has the potential to be automated to provide a rapid and consistently accurate supplementary identification system to traditional taxonomy. This project was a proof-of-concept study that investigated the feasibility of using DNA barcodes to differentiate closely related blackfly species of the genus Simulium. The longer term objective would be to apply such molecular approaches to organisms used in water quality monitoring and to biodiversity studies to provide a quick, robust but practical and cost effective tool for species identification. Great Britain is currently home to 33 morphospecies of blackfly many of which are morphologically close to other species and have been the cause of much systematic revision. In addition to evaluating the use of DNA barcodes in species identification, a non-destructive DNA extraction method was developed to preserve voucher pecimens that will allow a complete morphological classification to be carried after DNA extraction. Using molecular tools to differentiate closely related blackfly species of the genus Simulium v Finding an effective DNA barcode for an individual species involves accurate taxonomic identification and the retention of voucher specimens for future morphological studies. A rapid non-destructive method for DNA extraction from small insects was developed where no clean-up step was required prior to amplification and it was possible to extract DNA of sufficient quality in minutes retaining diagnostic morphological characteristics. For any molecular tool used for species discrimination, an important consideration is defining the specific genetic loci (e.g. the position of genes on a chromosome) to be monitored. All blackfly species in this study were successfully amplified with the standard barcoding coxI gene primer pair LCO1490 5'-GGT CAA CAA ATC ATA AAG ATA TTG G-3' and HCO2198 5'-TAA ACT TCA GGG TGA CCA AAA AAT CA-3' (Folmer et al. 1994) and we did not need to optimise or redesign the primer sequence.