DOI: 10.5061/dryad.15dv41p14
title: eBird data for: Avian behaviour changes in response to human activity during the COVID-19 lockdown in the United Kingdom

description: 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.
description: 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.
description: The .csv file can be opened in Microsoft Excel and R.
 The 'UK_eBird_DataPrep.R' code can be opened in R or
 Rstudio.