Mapping species distributions with social media geo-tagged images: Case studies of bees and flowering plants in Australia

Abstract Data sources on species distribution and range are typically expensive and time consuming to build, and traditional survey techniques often have spatial, temporal, or scale-related gaps. Social network sites, on the other hand, can provide massive amounts of cost effective data that may potentially yield information of direct benefit to supplement and understand ecological phenomena. Previous research explored using social network site content to enhance information collected by experts or professional surveys in domains including species distribution and land cover. However, the data quality and general suitability of social network sites data for answering questions related to species distribution and range is highly variable and this aspect of its value to science remains underexplored. In this research we investigate some causes of social network site data unreliability and explore how to mitigate them. We filter data points based on our estimates of reliability and relevance. We then use the filtered data to infer species ranges and distributions in concert with Global Biodiversity Information Facility (GBIF) data. Our proposed methodology was applied to four Australian case studies including two insect pollinators, and two flowering plants. The case studies were chosen from Australia because of its unique geographical features, large landmass, sparse population, and the many tourists and residents who travel across it taking photos and sharing them through social media. We show that, despite some barriers, there are instances where the social network site data clearly complement the existing source, making our technique a valuable means of making repeatable, efficient additions to traditional species distribution data.