Moths of Facebook: Enhancing professional collections with optimized citizen science data

Paper proposes workflow for optimizing opportunistic data and shows how optimized citizen science data improves performance of models based on professionally collected data

Data resources used via GBIF : 393 species occurrences
Biston perclarus
Biston perclarus by 林旭宏 via iNaturalist. Photo licensed under CC BY-NC 4.0.

Preferred for species distribution modelling, occurrence data derived from well-defined, controlled surveys is expensive and as consequence, often in short supply compared to the vast amounts of opportunistic data gathered by citizen science initiatives. Although the latter may be more prone to misidentification and biases, citizen science data–used correctly–can be a good supplement to professional surveys.

In a new study, researchers in Taiwan used GBIF-mediated occurrences from professionally curated datasets to test a novel optimization procedure for opportunistic data from a citizen science project on Facebook called EnjoyMoths. Using four different approaches, the authors modelled the distributions of nine moth species based on GBIF and EnjoyMoths datasets–both raw and optimized, separate and combined–while comparing the performance of the generated models.

Their results showed that the optimization procedure significantly improved the performance of models based on opportunistic data. Furthermore, the highest performing models were found among those based on GBIF data in combination with optimized opportunistic data. These findings suggest that the optimization approach is able to increase quality of such data, and highlights the value and potential of citizen science in biodiversity.

Link to original article

Lin Y-P, Lin W-C, Lien W-Y, Anthony J and Petway J (2017) Identifying Reliable Opportunistic Data for Species Distribution Modeling: A Benchmark Data Optimization Approach. Environments. MDPI AG 4(4): 81. Available at: