Sitting in your room, typing wikipedia.org/wiki/…/, you are actually helping scientists to monitor species outside the window. Recently in the open-access journal PLOS Biology, the University of Oxford, the University of Birmingham and Ben-Gurion University of the Negev collectively published their investigation on how Wikipedia pageview records correlates to seasonal patterns in nature. We had been unaware of how aware we are of natural phenomena.
Using a massive 3-year dataset of 2.33 billion page views for over 30,000 species across 245 Wikipedia language editions, the international team unearthed widespread seasonal trends in Wikipedia interest for many species. Over 25% of the species showed seasonality in pageviews. The amount and timing of internet activity turn out to be an accurate measure of when and how these seasonal species are present. To simply put, changes in presence and abundance of species are possible to be detected by our Wikipedia searches about them.
The seasonal interest in a species’ Wikipedia page reflects the seasonal pattern in the species themselves. Pageview for flowering plants surges during flowering season whereas that of coniferous trees had little noticeable changes. Similarly, most mammals do not have a significant seasonal pattern which is marked for insects and birds. Language editions provide insight into the seasonal patterns too. For Wikipedia editions in languages spoken in higher latitudes, such as Finnish or Norwegian, has greater seasonal interest in species. Whereas for Thai, Indonesia or other lower-latitude language editions has less seasonally marked browsing patterns.
This finding brings us good news: our online behaviour responds strongly to the natural world. Oxford researcher Richard Grenyer explains ‘by using these big data approaches we can begin to shortcut some of the more difficult problems, and cut to the core questions in modern conservation: how is the world changing, for which species is it changing the most, and where are the people who care the most and can do the most to help.’ Another potential application of big data approach is in conservation policy and actions. The selection of flagship species or iconic areas for conservation can be more effective with the help of big data. Identification of a seasonal interest peak in a certain species is essential for deciding when and how to launch a particular fundraising campaign.
Although we are far from accurately monitoring all the species across the globe, monitoring changes with pageviews presents us an alternative and also a conservation strategy for biodiversity.
