Detrending phenological time series improves climate-phenology analyses and reveals evidence of plasticity

• Published in: Ecology
• Main Authors: Iler, Amy M., Inouye, David W., Schmidt, Niels Martin, Høye, Toke Thomas
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2017
• Subjects: Arctic; Climate change; Confounded variables; Flowering phenology; Linear regression; Montane; Observational data; Phenological plasticity; Subalpine; Greenland
• Online Access: https://pure.au.dk/portal/en/publications/6abebcfc-719f-44d9-b980-59a5070a84e5; https://doi.org/10.1002/ecy.1690; https://pure.au.dk/ws/files/134618984/Detrending_phenological_time_series_improves_climate_phenology.pdf

Time series have played a critical role in documenting how phenology responds to climate change. However, regressing phenological responses against climatic predictors involves the risk of finding potentially spurious climate-phenology relationships simply because both variables also change across years. Detrending by year is a way to address this issue. Additionally, detrending isolates interannual variation in phenology and climate, so that detrended climate-phenology relationships can represent statistical evidence of phenotypic plasticity. Using two flowering phenology time series from Colorado, USA and Greenland, we detrend flowering date and two climate predictors known to be important in these ecosystems: temperature and snowmelt date. In Colorado, all climate-phenology relationships persist after detrending. In Greenland, 75% of the temperature-phenology relationships disappear after detrending (three of four species). At both sites, the relationships that persist after detrending suggest that plasticity is a major component of sensitivity of flowering phenology to climate. Finally, simulations that created different strengths of correlations among year, climate, and phenology provide broader support for our two empirical case studies. This study highlights the utility of detrending to determine whether phenology is related to a climate variable in observational data sets. Applying this as a best practice will increase our understanding of phenological responses to climatic variation and change.