Human activities may impact animal habitat and resource use, potentially influencing contemporary evolution in animals. In the United Kingdom (UK), COVID-19 lockdown restrictions resulted in sudden, drastic alterations to human activity. We hypothesized that short-term daily and long-term seasonal changes in human mobility might result in changes in bird habitat use, depending on the mobility type (home, parks, grocery) and the extent of change. Using Google human mobility data and 872 850 bird observations, we determined that during lockdown, human mobility changes resulted in altered habitat use in 80% (20/25) of our focal bird species. When humans spent more time at home, over half of affected species had lower counts, perhaps resulting from the disturbance of birds in garden habitats. Bird counts of some species (e.g. rooks, gulls) increased over the short-term as humans spent more time parks, possibly due to human-sourced food resources (e.g. picnic refuse), while counts of other species (e.g. tits and sparrows) decreased. All affected species increased counts when humans spent less time at grocery services. Avian species rapidly adjusted to the novel environmental conditions and demonstrated behavioural plasticity, but with diverse responses, reflecting the different interactions and pressures caused by human activity.

This dataset includes the ‘UK_eBird_data.csv’ file with eBird data extracted to use for the analysis of COVID impacts on bird counts in the UK. The code used to produce this file is in the R script file ‘UK_eBird_DataPrep.R’, which has also been extensively commented on to provide guidance for anyone trying to repeat the procedure. The file ‘UK_eBird_data.csv’ was filtered using a variety of ways (see the code for details). The file ‘UK_eBird_data.csv’ contains records of selected species (including zeros when those species were not recorded, i.e., ‘zero-filled’ data) from checklists that were spatially subsampled using the same hex-cell approach we used in the North American paper, except that none of the checklists from the single ‘power user’, who contributed tens of thousands of checklists himself, were included in that procedure. In addition, there are two columns in the data file, labeled ‘sub_region_1’ and ‘sub_region_2’ that should correspond to those same two named columns in Google mobility data files. For the main data file that sub-region is taken from the hex cell the checklist falls into, and for the power user data it comes from where the actual checklists were located. In addition, there are two columns in the data file, labeled ‘sub_region_1’ and ‘sub_region_2’ that should correspond to those same two named columns in Google mobility data files. For the main data file that sub-region is taken from the hex cell the checklist falls into, and for the power user data it comes from where the actual checklists were located.

The .csv file can be opened in Microsoft Excel and R.

The 'UK_eBird_DataPrep.R' code can be opened in R or Rstudio.