The utility of distribution data in predicting phenology

Abstract

The phenology of many species has been shown to shift under climate change. However, because species respond at different rates, ecological communities may be disrupted leading to species extinctions and loss of ecosystem services. Hence, there is a need to monitor and understand phenological change. Population data, gathered by standardised monitoring schemes, can be used to this end. However, such schemes require significant organisation and financial resources. Distribution data (georeferenced biological records with dates) are easier and cheaper to collect and may be an unexploited resource for phenology analyses. This would allow analysis of more taxa from more regions of the world. However, distribution data are potentially biased due to the unstandardised behaviour of biological recorders. Here, the ability of distribution data record dates to accurately predict phenology is investigated by using the British butterfly fauna as a model system. We used the total number of distribution records per unit time across Great Britain as a proxy for butterfly abundance. Phenology metrics of mean flight date and flight period length were then calculated from the resulting abundance?time relationships for each year in a 15-year time series. These estimates were validated against those generated from a standardised-effort population monitoring scheme. We analysed 1 078 328 records from 30 British butterflies and found that distribution data accurately predicted the mean flight date for 22 of the 30 species tested. Flight period length was only predicted accurately for seven of 30 species. We found a nonlinear but consistent positive relationship between the accuracy of mean flight date estimates and sample size (number of records) at both inter- and intraspecific scales. Our results suggest that a threshold sample size of c. 6500 distribution records (430 per year) is a pragmatic compromise between accuracy and recording effort, leading to little loss of accuracy in phenology predictions (an average decrease in accuracy of 2·9 days was observed). The results suggest that distribution data are a potentially useful resource for phenology research. This may allow practitioners to monitor particular regions and previously unstudied species relatively cheaply using existing mapping schemes.