Harnessing social media data to track species range shifts

dc.contributor.authorChowdhury, Shawan
dc.contributor.authorHawladar, Niloy
dc.contributor.authorRoy, Ripon C.
dc.contributor.authorCapinha, César
dc.contributor.authorCassey, Phillip
dc.contributor.authorCorreia, Ricardo A.
dc.contributor.authorDeme, Gideon Gywa
dc.contributor.authorDi Marco, Moreno
dc.contributor.authorDi Minin, Enrico
dc.contributor.authorJarić, Ivan
dc.contributor.authorLadle, Richard J.
dc.contributor.authorLenoir, Jonathan
dc.contributor.authorMomeny, Mohammad
dc.contributor.authorRinne, Jooel J.
dc.contributor.authorRoll, Uri
dc.contributor.authorBonn, Aletta
dc.contributor.organizationfi=Turun yliopiston biodiversiteettiyksikkö|en=Biodiversity Unit of the University of Turku|
dc.contributor.organization-code1.2.246.10.2458963.20.85536774202
dc.converis.publication-id515522034
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/515522034
dc.date.accessioned2026-04-24T16:14:34Z
dc.description.abstract<p>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.<br></p>
dc.identifier.eissn1523-1739
dc.identifier.jour-issn0888-8892
dc.identifier.urihttps://www.utupub.fi/handle/11111/58638
dc.identifier.urlhttps://doi.org/10.1111/cobi.70234
dc.identifier.urnURN:NBN:fi-fe2026022315431
dc.language.isoen
dc.okm.affiliatedauthorGywa, Gideon
dc.okm.affiliatedauthorHenriques Correia, Ricardo
dc.okm.discipline1181 Ecology, evolutionary biologyen_GB
dc.okm.discipline1181 Ekologia, evoluutiobiologiafi_FI
dc.okm.internationalcopublicationinternational co-publication
dc.okm.internationalityInternational publication
dc.okm.typeA1 ScientificArticle
dc.publisherWiley
dc.publisher.countryUnited Statesen_GB
dc.publisher.countryYhdysvallat (USA)fi_FI
dc.publisher.country-codeUS
dc.relation.articlenumbere70234
dc.relation.doi10.1111/cobi.70234
dc.relation.ispartofjournalConservation Biology
dc.titleHarnessing social media data to track species range shifts
dc.year.issued2026

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