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A social media-based framework for quantifying temporal changes to wildlife viewing intensity

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  • Papafitsoros, Kostas
  • Adam, Lukáš
  • Schofield, Gail

Abstract

Documenting how human pressure on wildlife changes over time is important to minimise potential adverse effects through implementing appropriate management and policy actions; however, obtaining objective measures of these changes and their potential impacts is often logistically challenging, particularly in the natural environment. Here, we developed a modular stochastic model that infers the ratio of actual viewing pressure on wildlife in consecutive time periods (years) using social media, as this medium is widespread and easily accessible. Pressure was calculated from the number of times individual animals appeared in social media in pre-defined time windows, accounting for time-dependent variables that influence them (e.g. number of people with access to social media). Formulas for the confidence intervals of viewing pressure ratios were rigorously developed and validated, and corresponding uncertainty was quantified. We applied the developed framework to calculate changes to wildlife viewing pressure on loggerhead sea turtles (Caretta caretta) at Zakynthos island (Greece) before and during the COVID-19 pandemic (2019–2021) based on 2646 social media entries. Our model ensured temporal comparability across years of social media data grouped in time window sizes, by correcting for the interannual increase of social media use. Optimal sizes for these windows were delineated, reducing uncertainty while maintaining high time-scale resolution. The optimal time window was around 7-days during the peak tourist season when more data were available in all three years, and >15 days during the low season. In contrast, raw social media data exhibited clear bias when quantifying changes to viewing pressure, with unknown uncertainty. The framework developed here allows widely-available social media data to be used objectively when quantifying temporal changes to wildlife viewing pressure. Its modularity allowed viewing pressure to be quantified for all data combined, or subsets of data (different groups, situations or locations), and could be applied to any site supporting wildlife exposed to tourism.

Suggested Citation

  • Papafitsoros, Kostas & Adam, Lukáš & Schofield, Gail, 2023. "A social media-based framework for quantifying temporal changes to wildlife viewing intensity," Ecological Modelling, Elsevier, vol. 476(C).
    • Handle: RePEc:eee:ecomod:v:476:y:2023:i:c:s0304380022003210
    DOI: 10.1016/j.ecolmodel.2022.110223
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    References listed on IDEAS

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    1. Devis Tuia & Benjamin Kellenberger & Sara Beery & Blair R. Costelloe & Silvia Zuffi & Benjamin Risse & Alexander Mathis & Mackenzie W. Mathis & Frank Langevelde & Tilo Burghardt & Roland Kays & Holger, 2022. "Perspectives in machine learning for wildlife conservation," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. David March & Kristian Metcalfe & Joaquin Tintoré & Brendan J. Godley, 2021. "Tracking the global reduction of marine traffic during the COVID-19 pandemic," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    3. Carolina Barros & Borja Moya-Gómez & Juan Carlos García-Palomares, 2019. "Identifying Temporal Patterns of Visitors to National Parks through Geotagged Photographs," Sustainability, MDPI, vol. 11(24), pages 1-16, December.
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