Impacts of COVID-19 restrictions on capacity to monitor bird populations: a case study using the UK Breeding Bird Survey

Abstract

ABSTRACT Capsule COVID-19 restrictions significantly biased BTO/JNCC/RSPB Breeding Bird Survey coverage across the UK allowing indicative trends to be produced for approximately one-third of species in England only. Aims To investigate the effect that COVID-19 restrictions had on participation in and coverage of the Breeding Bird Survey (BBS), and to quantify the likely impacts on population change reporting based on 2020 data. Methods We determined geographic, seasonal, and habitat coverage for the BBS in 2020 and compared this to previous years, and quantified the scale of biases and reductions in sample size for target species. We degraded existing BBS data (1994–2019) to simulate 2020 coverage and produced population change estimates using three methods applied to the complete and degraded data to assess the impacts of 2020 coverage on emergent trends. Results In 2020, 49% fewer survey squares were visited compared to 2019. Reductions were greatest in Wales, Scotland, and Northern Ireland, and in the early breeding season, when 90% fewer visits were made. The few early visits completed were on atypical dates and showed marked habitat biases. Individual species were detected in 23–96% fewer squares than normal. Population change estimates derived using routine trend models were negatively biased in up to 96% of species, with errors greatest for species normally detected on early visits. Alternative trend models using visit-specific parameterization or focussing only on late season visits overcame coverage biases for some species. Conclusions Lockdown restrictions associated with the COVID-19 outbreak meant it was not possible to produce population trend information for UK, Wales, Scotland, or Northern Ireland in 2020. Indicative long-term trends could be produced in England only for a subset of about 40 species. We recommend managers of citizen science schemes undertake similar analyses to assess the scale of coverage biases when unforeseen events cause temporary, but substantial changes, in sampling effort.