Integrating Social Media into Biodiversity Databases: The Next Big Step?

By Muhammad OsamaReviewed by Laura ThomsonMar 20 2025

A recent article published in Ecology and Evolution examined the occupancy of the Jersey tiger moth (Euplagia quadripunctaria) in urban habitats using social media data instead of traditional monitoring methods. The goal was to explore species responses to climate change in urban environments and assess the potential of non-traditional data sources for tracking distribution, highlighting the role of social media in ecological research.

Advancement in Biodiversity Monitoring

Digital technologies and social media have transformed ecology and conservation biology data collection. Traditional biodiversity monitoring often relies on field surveys, which can be time-consuming and biased toward rural habitats.

The Global Biodiversity Information Facility (GBIF) serves as a key repository for biodiversity data, but it faces challenges such as delayed data availability and underrepresentation of urban habitats.

Social media platforms have become valuable tools for rapid data collection, enabling users to share georeferenced observations instantly, reducing time lags associated with traditional methods. The widespread use of smartphones with cameras allows individuals to document wildlife sightings in real-time, enhancing biodiversity monitoring. Integrating social media data with traditional ecological datasets offers significant advancements, particularly in tracking species distributions in urban areas.

Using Social Media Data to Explore Habitats

In this paper, the authors evaluated the Jersey tiger moth's habitat usage by comparing occurrence data from social media platforms (Instagram and Flickr) with traditional records from GBIF and iNaturalist. They hypothesized that social media data would reveal significant JTM occurrences in urban environments, which may be underrepresented in traditional datasets.

The study analyzed records from 2000 to 2018, covering a broad geography, including the UK, the Republic of Ireland, and several mainland European countries.

The researchers used specific search terms to collect social media data and employed a custom script to extract georeferenced records from Flickr, while Instagram data was gathered manually. Recorder effort, defined as the ratio of species records to estimated abundance in a given location, was assessed using the Eurasian blackbird (Turdus merula) as a surrogate species due to its documentation across urban and rural environments.

Habitat suitability models (HSMs) were constructed using climatic variables such as maximum temperature and total precipitation to predict suitable habitats for JTM. The analysis compared habitat suitability predictions from GBIF with social media records to determine whether these alternative data sources could enhance insights into JTM's urban habitat usage. Generalized linear mixed models (GLMMs) were used to assess the influence of recorder effort on species distribution patterns.

Key Finding: Insights from Social Media Data

The outcomes showed significant differences in habitat usage between data sources. Instagram records of the Jersey tiger moth were predominantly found in urbanized areas with higher night light intensity, suggesting that social media data capture species occurrences overlooked by traditional monitoring. In contrast, GBIF and Flickr records were concentrated in rural habitats, indicating potential biases in conventional datasets. Statistical analyses indicated that Instagram records were linked to higher recorder effort, highlighting the platform's effectiveness in documenting species in urban environments.

While Flickr occurrences overlapped with GBIF records, they were less urban-focused than those from Instagram. The researchers emphasized the importance of considering recorder effort, as variations in recording practices influenced species distribution patterns. Notably, Instagram data revealed JTM occurrences in areas of low predicted habitat suitability according to GBIF models, suggesting that traditional monitoring methods may underestimate species' presence in urban environments.

The authors highlighted the role of social media in biodiversity monitoring, particularly for rapidly shifting species. The rapid availability of social media data can enhance species distribution assessments and inform conservation strategies. Overall, these results demonstrated the role of social media (Instagram) in filling gaps in traditional ecological datasets by capturing urban biodiversity trends often missed in conventional surveys.

Practical Applications

This research has significant implications for biodiversity monitoring and conservation. Integrating social media data with traditional methods can provide a more comprehensive understanding of species distributions, particularly in urban environments. This approach enables the identification of critical habitats for range-shifting species and supports conservation planning in rapidly urbanizing landscapes.

The findings highlight social media's potential to enhance citizen science initiatives by engaging the public in biodiversity documentation. Encouraging individuals to share wildlife observations can generate valuable data while fostering environmental awareness and community participation in conservation efforts. Incorporating social media data into formal biodiversity databases could improve the accuracy and scope of species distribution models.

Conclusion and Future Directions

Integrating social media data with traditional ecological datasets provided a comprehensive understanding of species distributions, particularly for species like the Jersey tiger moth. This study highlighted the ecological significance of urban environments, which are often underrepresented in conventional surveys. As urbanization expands globally, leveraging social media for biodiversity monitoring will be increasingly valuable.

Future work should focus on refining methodologies for assessing recorder effort and standardizing data extraction from social media platforms to enhance reliability and comparability. Applying this approach to a broader range of species and geographic regions could provide deeper insights into urban ecology and human-wildlife interactions. Advancing these methods will strengthen biodiversity monitoring and support more effective conservation strategies in rapidly changing environments.

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