A1 Refereed original research article in a scientific journal
Harnessing social media data to track species range shifts
Authors: Chowdhury, Shawan; Hawladar, Niloy; Roy, Ripon C.; Capinha, César; Cassey, Phillip; Correia, Ricardo A.; Deme, Gideon Gywa; Di Marco, Moreno; Di Minin, Enrico; Jarić, Ivan; Ladle, Richard J.; Lenoir, Jonathan; Momeny, Mohammad; Rinne, Jooel J.; Roll, Uri; Bonn, Aletta
Publisher: Wiley
Publication year: 2026
Journal: Conservation Biology
Article number: e70234
ISSN: 0888-8892
eISSN: 1523-1739
DOI: https://doi.org/10.1111/cobi.70234
Publication's open availability at the time of reporting: Open Access
Publication channel's open availability : Partially Open Access publication channel
Web address : https://doi.org/10.1111/cobi.70234
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/515522034
Self-archived copy's licence: CC BY
Self-archived copy's version: Publisher`s PDF
Biodiversity monitoring programs and citizen science data remain heavily biased toward theGlobal North. Especially in megadiverse countries with limited biodiversity records, incor-porating social media data can help address existing data gaps. However, whether such datacan significantly improve our understanding of range-shifting species is still unknown. Wetested whether social media data improved our knowledge of the range dynamics of a rapidrange-shifting butterfly, the tawny coster (Acraea terpsicore). We collated locality data fromFlickr and Facebook and compared these with occurrence data from the Global Biodiver-sity Information Facility (GBIF). We used species distribution models (SDMs) and nicheassessments, which we calibrated with data from GBIF alone and both sources combined (GBIF and social media data) to analyze range shift dynamics. Social media data increasedoccurrence records by 35%, and the proportion of social media data was higher in coun-tries poorly represented in GBIF. In addition, we obtained new distributional informationfrom well-represented countries (e.g., Australia and Malaysia). Over time, the SDMs cali-brated with GBIF and social media data showed greater expansion rates than SDMs basedsolely on GBIF data. The niche assessments revealed that GBIF-only data failed to captureregions with relatively low maximum temperature, relatively low precipitation and high ele-vation. Our results highlight the potential of harnessing social media data to track rapidbiodiversity redistribution in response to climate change.
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Funding information in the publication:
A.B. gratefully acknowledge the support of the German Centre for Integrative Biodiversity Research (iDiv) and the sMon project funded by the German Research Foundation (through grant nos. DFG-FZT 118 and 202548816). P.C. is an Australian Research Industry Laureate Fellow (grant title Combatting Wildlife Crime and Preventing Environmental Harm, IL230100175). R. A. C. acknowledges support from the Research Council of Finland (grant agreement no. 348352) and the KONE Foundation (grant agreement no. 202101976). GGD acknowledges the support from the British Ecological Society (through grant no. CE24/1043). EDM was funded by the European Union (ERC, BIOBANG, 101171602) and the KONE Foundation (project 202309134). RJL was supported by CNPq (grants 447598/2025-2, 441125/2023-9, 306174/2025-1).
