Using citizen science butterfly counts to predict species population trends

Citizen scientists are increasingly engaged in gathering biodiversity information, but trade-offs are often required between public engagement goals and reliable data collection. We compare population estimates derived from the first four years (2011-2014) of a short-duration citizen science project (Big Butterfly Count, BBC), to those from long-running, standardized monitoring data collected by experienced observers (UK Butterfly Monitoring Scheme, UKBMS), for 18 widespread butterfly species. BBC data are gathered during an annual, three-week period, whereas UKBMS sampling takes place over six months each year. An initial comparison with UKBMS data restricted to the three-week BBC period revealed that species population changes were significantly correlated between the two sources. The short-duration sampling season renders BBC counts susceptible to bias caused by inter-annual phenological variation in the timing of species’ flight periods. BBC counts were found to be described well by measures for phenology and sampling effort. Annual estimates of species abundance and population trends predicted from models including BBC data and weather covariates as a proxy for phenology correlated significantly with those derived from UKBMS data. In validating the BBC counts, we show, for the first time, that citizen science data, obtained using a simple sampling protocol, can produce comparable estimates of insect species abundance to standardized monitoring data. Although caution is urged in extrapolating from this UK study of a small number of common, conspicuous insects, we demonstrate that mass-participation citizen science can simultaneously contribute to public engagement and biodiversity monitoring. Mass-participation citizen science is not an adequate replacement for standardised biodiversity monitoring but may have a role in extending and complementing it (e.g. by sampling different land-use types), as well as serving to reconnect an increasingly urban human population with nature.