Birdwatchers' resilience to perturbation in India buffers citizen science from pandemic-induced biases

Abstract

Most systematic projects to monitor bird populations, like breeding bird surveys, require large and coordinated volunteer networks that are lacking in many parts of the world such as the Global South. Data from less systematic citizen science (CS) programmes offer an alternative to data from systematic initiatives in these regions, but the semi-structured nature of such data also presents several challenges. The utility of semi-structured CS data to monitor bird species abundance is contingent on how, where, and how comparably birdwatchers watch birds, year on year. Trends inferred directly from the data can be confounded during years when birdwatchers may behave differently, such as during the COVID-19 pandemic. We wanted to ascertain how the data uploaded from India to one such CS platform, eBird, was impacted by this deadliest global pandemic of the 21st century. To understand whether eBird data from the pandemic years in India is useful and comparable to data from adjacent years, we explored several quantitative and qualitative aspects of the data (such as birdwatcher behaviour) at multiple spatial and temporal scales. We found no negative impact of the pandemic on data generation. Data characteristics changed largely only during the peak pandemic months characterised by high fatality rates and strict lockdowns, possibly due to decreased human mobility and social interaction. It remained similar to the adjacent years during the rest of this restrictive period, thereby reducing the impact of the aberrant peak months on any annual inference. Moreover, impacts on data characteristics varied widely across states in India, resulting in no strong consistent trend at the national level--unlike results from elsewhere in the world. Our findings show that birdwatchers in India as contributors to CS were resilient to disturbance, and that the effects of the pandemic on birdwatching effort and birdwatcher behaviour are highly scale- and context-dependent. In summary, eBird data in India from the pandemic years remains useful and interpretable for most large-scale applications, such as abundance trend estimation, but will benefit from preliminary data quality checks when utilised at a fine scale. Lay summaryO_LICitizen science platforms like eBird comprise vast repositories of data generated by casual birdwatching. C_LIO_LISuch data are vital to understanding bird population trends, but their usability reduces when birdwatchers change where, when and how much they watch birds from year to year. C_LIO_LIGiven the impact of the COVID-19 pandemic on our everyday lives, we wondered whether it also impacted the way people reported birds, thereby reducing the usability of the data in trend analyses. C_LIO_LIWe analysed data uploaded to eBird from India, and found that the impacts of the pandemic on this data were largely restricted to April and May in 2020, and to a lesser C_LIO_LIextent in 2021. During these months that coincided with the greatest health impacts, birdwatchers avoided travel, groups and public spaces. C_LIO_LIBirdwatchers were resilient; they bounced back soon after these difficult periods, and started birding like they had done before the pandemic. C_LIO_LIBecause the impact was limited to short periods and few regions, we conclude that eBird data from India during the pandemic still remains useful for analyses of bird abundance trends. C_LI