Information visualisation for science and policy: engaging users and avoiding bias.

Visualisations and graphics are fundamental to studying complex subject matter. However, beyond acknowledging this value, scientists and science-policy programmes rarely consider how visualisations can enable discovery, create engaging and robust reporting, or support online resources. Producing accessible and unbiased visualisations from complicated, uncertain data requires expertise and knowledge from science, policy, computing, and design. However, visualisation is rarely found in our scientific training, organisations, or collaborations. As new policy programmes develop [e.g., the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES)], we need information visualisation to permeate increasingly both the work of scientists and science policy. The alternative is increased potential for missed discoveries, miscommunications, and, at worst, creating a bias towards the research that is easiest to display.

Significance of sighting rate in inferring extinction and threat.

We are now entering a time of immense environmental upheaval in which, increasingly, experts are required to provide conservation assessments. Quantitative assessment of trends in species' range and abundance is costly, requiring extensive field studies over a long period of time. Unfortunately, many species are only known through a few "chance" sightings or a handful of specimens, and therefore extinction may be even harder to ascertain. Several methods have been proposed for estimating the probability of extinction. However comparison within and between species is difficult because of variations in sighting rates. We applied a probabilistic method that incorporates sighting rate to the sighting record of Vietnamese slipper orchids (Paphiopedilum). The method generates a probability that another sighting will occur given the previous sighting rate and the time since last observation. This allows greater comparability between species discovered at different times. Its predictions were more highly correlated with the World Conservation Union criteria than previous methods. Trends in data collection and the political climate of a country, which affects access to material, are important potential sources of variation that affect sighting rates. A lack of understanding of the process by which data are generated makes inferring extinction from sighting records difficult because extinction status depends on how the sighting rate varies. However such methods allow rapid conservation prioritization of taxa that are poorly known and would otherwise go unassessed.