Lagged and dormant season climate better predict plant vital rates than climate during the growing season. (15th February 2021) - Evers, Sanne M.; Knight, Tiffany M.; Inouye, David W.; Miller, Tom E. X.; Salguero‐Gómez, Roberto; Iler, Amy M.; Compagnoni, Aldo
Abstract: Understanding the effects of climate on the vital rates (e.g., survival, development, reproduction) and dynamics of natural populations is a long‐standing quest in ecology, with ever‐increasing relevance in the face of climate change. However, linking climate drivers to demographic processes requires identifying the appropriate time windows during which climate influences vital rates. Researchers often do not have access to the long‐term data required to test a large number of windows, and are thus forced to make a priori choices. In this study, we first synthesize the literature to assess current a priori choices employed in studies performed on 104 plant species that link climate drivers with demographic responses. Second, we use a sliding‐window approach to investigate which combination of climate drivers and temporal window have the best predictive ability for vital rates of four perennial plant species that each have over a decade of demographic data ( Helianthella quinquenervis, Frasera speciosa, Cylindriopuntia imbricata, and Cryptantha flava ). Our literature review shows that most studies consider time windows in only the year preceding the measurement of the vital rate(s) of interest, and focus on annual or growing season temporal scales. In contrast, our sliding‐window analysis shows that in only four out of 13 vital rates the selected climate drivers have time windows that align with, or are similar to, the growing season. For many vital rates, the best window lagged more than 1 year and up to 4 years before the measurement of the vital rate. Our results demonstrate that for the vital rates of these four species, climate drivers that are lagged or outside of the growing season are the norm. Our study suggests that considering climatic predictors that fall outside of the most recent growing season will improve our understanding of how climate affects population dynamics. Abstract : Selecting the right time‐windows is vital to understand how plant populations respond to climate change. We found that most of the recent literature uses one type of time‐window: the recent growing season. However, using four long‐term datasets, we find that model selection also supports time‐windows in the dormant season, and in preceding years.