Meaningful species population trends from mass-participation citizen science: the Big Butterfly Count example
Citizen science, the involvement of members of the public in gathering data and undertaking research, is flourishing in the UK and around the world, particularly as a means of monitoring wildlife and the environment at a large scale1. Knowledgeable amateurs have long formed the backbone of biological recording in the UK2, but much of the recent upsurge in citizen science has involved mass-participation projects, which seek to engage people with little or no previous experience of biodiversity monitoring. Butterfly Conservation’s Big Butterfly Count is one such project, with 100,000 participants in 2018 and over 296,793 since it began in 2010.
While the scientific community and policy makers acknowledge the validity and importance of data gathered by volunteers as part of standardised, rigorous monitoring programmes such as the UK Butterfly Monitoring Scheme (UKBMS), many remain wary of the data quality and, therefore, results of mass-participation citizen science3. Their concerns stem both from the involvement of large numbers of often inexperienced members of the public, which may lead to species identification errors, and the use of simple sampling techniques, which are necessary to enable untrained volunteers to take part.
Big Butterfly Count data are affected by both these sources of error. Although focussed on the most common UK butterflies, inexperienced recorders undoubtedly make identification mistakes and no expert verification of species records is undertaken prior to analysis. Furthermore, the very simple sampling protocol, a 15-minute count in any sunny place, introduces bias due to spatial and temporal variation in recording effort4.
These issues could render the species abundance data gathered by Big Butterfly Count meaningless and restrict the value of the project to raising awareness and public engagement. However, a recent study by Butterfly Conservation, the University of Kent and the Centre for Ecology & Hydrology, suggests this is not the case. Leading the research, Dr Emily Dennis showed that despite all the potential pitfalls in the Big Butterfly Count methodology, the scheme could produce butterfly population trends that closely matched those produced by the rigorous sampling and skilled volunteers of the UKBMS5.
Looking just at records from the three-week period in which Big Butterfly Count takes place, species population growth rates derived from the UKBMS were strongly correlated with those from Big Butterfly Count over the years analysed (2011-2014). Of course, butterfly flight periods shift from year to year depending on the weather (many were early in 2018, for example, due to the prolonged hot weather) and this presents a problem for a short-term ‘snap-shot’ survey such as Big Butterfly Count. In some years the Count period may fall right on the peak in numbers of a particular species, but in others it may take place when populations are only just starting to emerge or already ebbing naturally. The study found that the impact of these natural variations on the Big Butterfly Count trends could be corrected using weather data.
Species trends derived from opportunistic biological records, such as those collated for atlas projects and through online databases, have been found to correlate with trends from structured, standardised monitoring for butterflies6 and birds7, but this has not previously been shown for a mass-participation citizen science project like Big Butterfly Count.
Thus, the findings of Dennis et al. establish Big Butterfly Count as a rare example, to date, of a citizen science biodiversity project that has achieved both large-scale outreach/public engagement and meaningful scientific output – two goals often considered to trade-off against each other8.
Richard Fox
Associate Director Recording and Research
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2. Pocock, M.J.O., Roy, H.E., Preston, C.D. & Roy, D.B. (2015) The Biological Records Centre: a pioneer of citizen science. Biological Journal of the Linnean Society 115:475–493. doi:10.1111/bij.12548
3. Kosmala, M., Wiggins, A., Swanson, A. & Simmons, B. (2016) Assessing data quality in citizen science. Frontiers in Ecololgy and the Environment 14: 551–560. doi:10.1002/fee.1436
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5. Dennis, E.B., Morgan, B.J.T., Brereton, T.M., Roy, D.B. & Fox, R. (2017) Using citizen science butterfly counts to predict species population trends. Conservation Biology 31: 1350–1361. doi:10.1111/cobi.12956
6. Warren, M.S., Hill, J.K., Thomas, J.A., Asher, J., Fox, R., Huntley, B., Roy, D.B., Telfer, M.G., Jeffcoate, S., Harding, P., Jeffcoate, G., Willis, S.G., Greatorex-Davies, J.N., Moss, D. & Thomas, C.D. (2001) Rapid responses of British butterflies to opposing forces of climate and habitat change. Nature 414: 65–69. doi:10.1038/35102054
7. Horns, J.J., Adler, F.R. & Şekercioğlu, Ç.H. (2018) Using opportunistic citizen science data to estimate avian population trends. Biological Conservation 221: 151–159. doi:10.1016/j.biocon.2018.02.027
8. Lakeman-Fraser, P., Gosling, L., Moffat, A.J., West, S.E., Fradera, R., Davies, L., Ayamba, M.A. & van der Wal, R. (2016) To have your citizen science cake and eat it? Delivering research and outreach through Open Air Laboratories (OPAL). BMC Ecology 16(Suppl 1): 57–70. doi:10.1186/s12898-016-0065-0