A comparison of herbarium and citizen science phenology datasets for detecting response of flowering time to climate change in Denmark.

Phenology has emerged as a key metric to measure how species respond to changes in climate. Innovative means have been developed to extend the temporal and spatial range of phenological data by obtaining data from herbarium specimens, citizen science programs, and biodiversity data repositories. These different data types have seldom been compared for their effectiveness in detecting environmental impacts on phenology. To address this, we compare three separate phenology datasets from Denmark: (i) herbarium specimen data spanning 145 years, (ii) data collected from a citizen science phenology program over a single year observing first flowering, and (iii) data derived from incidental biodiversity observations in iNaturalist over a single year. Each dataset includes flowering day of year observed for three common spring-flowering plant species: Allium ursinum (ramsons), Aesculus hippocastanum (horse chestnut), and Sambucus nigra (black elderberry). The incidental iNaturalist dataset provided the most extensive geographic coverage across Denmark and the largest sample size and recorded peak flowering in a way comparable to herbarium specimens. The directed citizen science dataset recorded much earlier flowering dates because the program objective was to report the first flowering, and so was less compared to the other two datasets. Herbarium data demonstrated the strongest effect of spring temperature on flowering in Denmark, possibly because it was the only dataset measuring temporal variation in phenology, while the other datasets measured spatial variation. Herbarium data predicted the mean flowering day of year recorded in our iNaturalist dataset for all three species. Combining herbarium data with iNaturalist data provides an even more effective method for detecting climatic effects on phenology. Phenology observations from directed and incidental citizen science initiatives will increase in value for climate change research in the coming years with the addition of data capturing the inter-annual variation in phenology.