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Ecological Modelling ; 476, 2023.
Article in English | Scopus | ID: covidwho-2244053


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. © 2022 The Author(s)

IOP Conference Series Earth and Environmental Science ; 1122(1):012007, 2022.
Article in English | ProQuest Central | ID: covidwho-2188015
Zhongguo Yufang Shouyi Xuebao / Chinese Journal of Preventive Veterinary Medicine ; 44(3):344-344, 2022.
Article in English, Chinese | CAB Abstracts | ID: covidwho-2034013
American Journal of Primatology ; 84(4/5), 2022.
Article in English | CAB Abstracts | ID: covidwho-2033684
Sustainability ; 14(17):10791, 2022.
Article in English | ProQuest Central | ID: covidwho-2024201
7. ; 2020.
Article in English | ProQuest Central | ID: covidwho-2011154
Journal of Fish and Wildlife Management ; 13(1):81, 2022.
Article in English | ProQuest Central | ID: covidwho-1903589
Oryx ; 56(3):334-335, 2022.
Article in English | ProQuest Central | ID: covidwho-1815409
Journal of Applied Animal Welfare Science ; 25(2):119-125, 2022.
Article in English | ProQuest Central | ID: covidwho-1806033
Canadian Journal of Zoology ; 100(3):176-183, 2022.
Article in English | Academic Search Complete | ID: covidwho-1714434
Pure and Applied Biology ; 11(1):11-25, 2022.
Article in English | ProQuest Central | ID: covidwho-1596497