Beyond seasonal climate: statistical estimation of phenological responses to weather. Issue 7 (1st October 2014)

• Record Type: Journal Article
• Authors: Diez, Jeffrey M.; Ibáñez, Inés; Silander, John A.; Primack, Richard; Higuchi, Hiroyoshi; Kobori, Hiromi; Sen, Ananda; James, Timothy Y.
• Is Part Of: Ecological applications. Volume 24:Issue 7(2014)
• Page Start: 1793
• Page End: 1802
• Publication Date: 2014-10-01
• Subjects: climate -- daily models -- frost events -- phenology -- thermal forcing -- weather events; Ecology -- Periodicals; Environmental protection -- Periodicals; Biology, Economic -- Periodicals; 577.05
• DOI: 10.1890/13-1533.1
• Languages: English
• ISSNs: 1051-0761

Abstract

Abstract : Phenological events, such as the timing of flowering or insect emergence, are influenced by a complex combination of climatic and non‐climatic factors. Although temperature is generally considered most important, other weather events such as frosts and precipitation events can also influence many species' phenology. Non‐climatic variables such as photoperiod and site‐specific habitat characteristics can also have important effects on phenology. Forecasting phenological shifts due to climate change requires understanding and quantifying how these multiple factors combine to affect phenology. However, current approaches to analyzing phenological data have a limited ability for quantifying multiple drivers simultaneously. Here, we use a novel statistical approach to estimate the combined effects of multiple variables, including local weather events, on the phenology of several taxa (a tree, an insect, and a fungus). We found that thermal forcing had a significant positive effect on each species, frost events delayed the phenology of the tree and butterfly, and precipitation had a positive effect on fungal fruiting. Using data from sites across latitudinal gradients, we found that these effects are remarkably consistent across sites once latitude and other site effects are accounted for. This consistency suggests an underlying biological response to these variables that is not commonly estimated using data from field observations. This approach's flexibility will be useful for forecasting ongoing phenological responses to changes in climate variability in addition to seasonal trends.