Increasing biodiversity knowledge through social media: A case study from tropical Bangladesh

Abstract

Citizen science programs are becoming increasingly popular among naturalists but remain heavily biased taxonomically and geographically. However, with the explosive popularity of social media and the near-ubiquitous availability of smartphones, many post wildlife photographs on social media. Here, we illustrate the potential of harvesting these data to enhance our biodiversity understanding using Bangladesh, a tropical biodiverse country, as a case study. We compared biodiversity records extracted from Facebook with those from the Global Biodiversity Information Facility (GBIF), collating geospatial records for 1013 unique species, including 970 species from Facebook and 712 species from GBIF. Although most observation records were biased toward major cities, the Facebook records were more evenly spatially distributed. About 86% of the Threatened species records were from Facebook, whereas the GBIF records were almost entirely Of Least Concern species. To reduce the global biodiversity data shortfall, a key research priority now is the development of mechanisms for extracting and interpreting social media biodiversity data.

Keywords: Facebook; Wallacean shortfall; biodiversity conservation; citizen science; social media data.

Figure 1.

Distribution (a) and taxonomic group-wise number (b) of geospatial records for Bangladeshi animals using records from both Facebook (F) and GBIF (G). The photographs are some species for which we only obtained distribution records from Facebook. The source of these photographs are the following: Ms Sarita Jnawali (Manis pentadactyla), Gerard Chartier (Euploea crameri), Charles J Sharp (Gavialis gangeticus), Jason Thompson (Tringa guttifer), and Shawan Chowdhury (Kallima inachus).

Figure 2.

The difference in extent of occurrence (EOO, in square kilometers) for Bangladeshi animals using geospatial records from Facebook and GBIF. The dashed line indicates where the EOO estimates from Facebook and GBIF were identical.