Estimating phenological sensitivity in contemporary vs. historical data sets: Effects of climate resolution and spatial scale

• Published in: American Journal of Botany
• Main Authors: Zettlemoyer, Meredith A., Wilson, Jill E., DeMarche, Megan L.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2022
• Subjects: Silene acaulis
• Online Access: http://dx.doi.org/10.1002/ajb2.16087; https://onlinelibrary.wiley.com/doi/pdf/10.1002/ajb2.16087; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ajb2.16087

Abstract Premise Phenological sensitivity, or the degree to which a species' phenology shifts in response to warming, is an important parameter for comparing and predicting species' responses to climate change. Phenological sensitivity is often measured using herbarium specimens or local studies in natural populations. These approaches differ widely in spatiotemporal scales, yet few studies explicitly consider effects of the geographic extent and resolution of climate data when comparing phenological sensitivities quantified from different data sets for a given species. Methods We compared sensitivity of flowering phenology to growing degree days of the alpine plant Silene acaulis using two data sets: herbarium specimens and a 6 yr observational study in four populations at Niwot Ridge, Colorado, USA. We investigated differences in phenological sensitivity obtained using variable spatial scales and climate data sources. Results Herbarium specimens underestimated phenological sensitivity compared to observational data, even when herbarium samples were limited geographically or to nearby weather station data. However, when observational data were paired with broader‐scale climate data, as is typically used in herbarium data sets, estimates of phenological sensitivity were more similar. Conclusions This study highlights the potential for variation in data source, geographic scale, and accuracy of macroclimate data to produce very different estimates of phenological responses to climate change. Accurately predicting phenological shifts would benefit from comparisons between methods that estimate climate variables and phenological sensitivity over a variety of spatial scales.