Eu-Social Science: The Role of Internet Social Networks in the Collection of Bee Biodiversity Data

Background Monitoring change in species diversity, community composition and phenology is vital to assess the impacts of anthropogenic activity and natural change. However, monitoring by trained scientists is time consuming and expensive. Methodology/Principal Findings Using social networks, we assess whether it is possible to obtain accurate data on bee distribution across the UK from photographic records submitted by untrained members of the public, and if these data are in sufficient quantity for ecological studies. We used Flickr and Facebook as social networks and Flickr for the storage of photographs and associated data on date, time and location linked to them. Within six weeks, the number of pictures uploaded to the Flickr BeeID group exceeded 200. Geographic coverage was excellent; the distribution of photographs covered most of the British Isles, from the south coast of England to the Highlands of Scotland. However, only 59% of photographs were properly uploaded according to instructions, with vital information such as ‘tags’ or location information missing from the remainder. Nevertheless, this incorporation of information on location of photographs was much higher than general usage on Flickr (∼13%), indicating the need for dedicated projects to collect spatial ecological data. Furthermore, we found identification of bees is not possible from all photographs, especially those excluding lower abdomen detail. This suggests that giving details regarding specific anatomical features to include on photographs would be useful to maximise success. Conclusions/Significance The study demonstrates the power of social network sites to generate public interest in a project and details the advantages of using a group within an existing popular social network site over a traditional (specifically-designed) web-based or paper-based submission process. Some advantages include the ability to network with other individuals or groups with similar interests, and thus increasing the size of the dataset and participation in the project

Use of confidence radii to visualise significant differences in principal components analysis: Application to mammal assemblages at locations with different disturbance levels

Multivariate statistical analysis is a powerful method of examining complex datasets, such as species assemblages, that does not suffer from the oversimplification prevalent in many univariate analyses. However, identifying whether data points on a multivariate plot are clustered is subjective, as there is no determination of significant differences between the points and no indication of the level of confidence in those points. The validity of drawing such conclusions may therefore be considered suspect. This paper describes a method of bootstrapping calculated principal components to estimate a confidence radius, similar to confidence intervals in univariate techniques. Plotting 3D scatterplots of the principal components, with the size of the spherical point representative of the level of confidence of the estimate, gives a clear and visual indication of significant difference between the points — where the spheres overlap there is no significant difference. We apply the technique to mammal assemblages at sites in Epping Forest (Essex, UK) that differ in the level of disturbance present and find that differences between some sites that appear large using traditional principal components analysis are actually not significantly different at the 95% confidence level, while other sites do differ significantly. Sites that differ most in anthropogenic disturbance are not significantly different in terms of assemblage structure

Children living with HIV in Europe: do migrants have worse treatment outcomes?

International audienceTo assess the effect of migrant status on treatment outcomes among children living with HIV in Europe