Title: Land Surface Phenology and Climate Variation: Green-up of Deciduous Forest
Communities of Northeastern North America
Author(s): Xie Y., Allen J., Wilson A. and Silander J.
Affiliation/Institution: University of Connecticut, Department of Ecology and
Evolutionary Biology
Contact email: yingying.xie@uconn.edu
Presentation type: A: Oral
Abstract:
The shifts in plant phenology detected globally in recent years provide important
biotic indicators of climate change. Land Surface Phenology (LSP) from remote
sensing offers important sources of data of vegetation phenology at landscape to
global spatial scales. Yet rarely is the mechanistic basis of plant physiological
responses to chilling and heating requirements considered at broad spatial scales.
Geographical gradients in community composition may also affect LSP predictions
spatially and temporally. Survival analysis models used in this study show the effects
of weather and climate, as these relate to plant physiological chilling and heating
requirements, affect deciduous forest green-up in New England, USA over nine years
(2001-2009). Furthermore, the effects of topographic variation and differences in
forest species composition across the landscape are also modeled. Results show
significant effects of all factors, but especially chilling requirements. While warmer
springs lead to earlier green-up of deciduous forest, if chilling unit requirements are
not met, later green-up dates occur. We also found earlier green-up on eastern and
southern slope aspects, but later green-up at northern and western aspect slopes. Due
to different phenological responses among species of canopy trees, species
composition also significantly affects community and landscape phenology. Across
sites, greater oak dominance leads to later green-up, and maple dominance promote
smaller effect, while sites with greater birch dominance show earlier green-up.
Overall the probability of green-up dates for deciduous forests across New England at
each time step can be accurately estimated by this modeling approach along with the
uncertainty thereof. This study provides an innovative statistical method combining
plant physiological mechanisms and spatial heterogeneity in topography and species
composition to predict how LSP responds to climate and weather variation. Using this
approach it is thus possible to predict phenological responses to forecasts from
climate change projections in the future.