Mapping bat hunting, trade and consumption in Indonesia using records from online platforms

Hunting, trade and consumption (exploitation) of bats for food, medicine or other uses is widespread and threatens many species worldwide. However, collecting exploitation data in the field is logistically challenging and resource‐intensive, resulting in gaps in our knowledge of the extent and intensity of exploitation in many areas. Online platforms, such as social media, provide a new data source from which to better understand spatial patterns of human–wildlife interactions. Nonetheless, online records can be biased and often vary in spatial and taxonomic resolution. This study aimed to investigate the effectiveness of using human–nature interface mapping, whereby statistical approaches from species distribution modelling in building spatially explicit threat maps with online data. We predicted the probability of bat exploitation occurrence across Indonesia, using 475 records obtained from automated searches in English and Indonesian. Overall, MaxEnt models showed high performance, with an average AUC of 0.89. The use of bias layers to select background data did not consistently improve model performance when this was assessed using cross‐validation, but it did slightly improve performance when this was assessed using a held‐out dataset of threat occurrence points from academic literature. Predictions from models of trade and consumption occurrence had relatively low similarity (Pearson's correlation = 0.586) with predictions from models of hunting occurrence, and hunting was predicted to occur over a more extensive area. This study demonstrates the utility of using presence‐only modelling to produce spatially explicit predictions of human–nature interactions. These models can be combined with locally relevant information and ground‐truthing to inform the threat status of bat populations in Indonesia and prioritise conservation interventions. Read the free Plain Language Summary for this article on the Journal blog.