Data from: Despite short-lived changes, COVID-19 pandemic had minimal large-scale impact on citizen science participation in India

This dataset contains the filtered and processed data files used in the analyses for the paper titled "Despite short-lived changes, COVID-19 pandemic had minimal large-scale impact on citizen science participation in India". These are .Rdata files that can be readily imported into the R environment (all analyses were performed in the R environment using RStudio).

It is important to note that these data files were derived from publicly available datasets, and that the entire analytical workflow is reproducible (see the Github repository for more details). Below is a summary of the datasets used:

Data

The analysis is centred around eBird data from India, which is publicly available but is a very large file.

eBird Basic Dataset (EBD), relMay-2022: The current version of this dataset is much more recent and contains data after May 2022, but filtering it should produce a fairly similar dataset to the ones used in this study, with only minor changes. (The processed version of this EBD ready for analyses is data0_MY_d_slice.RData).

Shapefiles: maps_sf.RData contains admin unit polygons, and grids_st_sf.RData contains country-wide grids of multiple resolutions.

MODIS Land Cover Type Product MCD12Q1: The processed data is available as rast_UNU.RData, but deleting this before starting the analysis will force fresh LULC data files from MODIS to be produced from the script.

Timeline of COVID classification: Available covid_classification.csv. Classification of the timeline of interest into various COVID categories.

Abstract

Many parts of the world lack the large and coordinated volunteer networks required for systematic monitoring of bird populations. In these regions, citizen science (CS) programmes offer an alternative with their semi-structured data, but the utility of these data is contingent on how, where, and how comparably birdwatchers watch birds, year on year. Trends inferred 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, were impacted by this deadly global pandemic. To understand whether eBird data from the pandemic years in India are comparable to data from adjacent years, we explored several characteristics of the data, such as how often people watched birds in groups or at public locations, at multiple spatial and temporal scales. We found that the volume of data generated increased during the pandemic years 2020--21 compared to 2019. Data characteristics changed largely only during the peak pandemic months (April--May 2020 and April--May 2021) associated with high fatality rates and strict lockdowns. These changes in data characteristics (e.g., greater site fidelity and less group birding) were possibly due to the decreased human mobility and social interaction in these periods. The data from the remainder of these restrictive years remained similar to those of the adjacent years, thereby reducing the impact of the aberrant peak months on any annual inference. Our findings show that birdwatchers in India as contributors to CS rapidly returned to their pre-pandemic behaviour, and that the effects of the pandemic on birdwatching effort and birdwatcher behaviour are scale- and context-dependent. In summary, eBird data from the pandemic years in India remain useful for abundance trend estimation and similar large-scale applications, but will benefit from preliminary data quality checks when utilised at a fine scale.