Predicting Maximum Tree Heights and Other Traits from Allometric Scaling and Resource Limitations
Name
Kempes-2011-Predicting Maximum T.pdf
Size
723.61 KB
Format
Adobe PDF
Checksum (MD5)
c394710d6ad275f4f75e1604795c9e74
Author(s)
Kempes, Chris Poling
West, Geoffrey B.
Crowell, Kelly
Girvan, Michelle
Date Issued
June 2011
Journal
PLoS One
Publisher
Public Library of Science
Citation
Kempes, Christopher P. et al. “Predicting Maximum Tree Heights and Other Traits from Allometric Scaling and Resource Limitations.” Ed. Darren Mark Evans. PLoS ONE 6 (2011): e20551.
Version
Final published version
Abstract
Terrestrial vegetation plays a central role in regulating the carbon and water cycles, and adjusting planetary albedo. As such, a clear understanding and accurate characterization of vegetation dynamics is critical to understanding and modeling the broader climate system. Maximum tree height is an important feature of forest vegetation because it is directly related to the overall scale of many ecological and environmental quantities and is an important indicator for understanding several properties of plant communities, including total standing biomass and resource use. We present a model that predicts local maximal tree height across the entire continental United States, in good agreement with data. The model combines scaling laws, which encode the average, base-line behavior of many tree characteristics, with energy budgets constrained by local resource limitations, such as precipitation, temperature and solar radiation. In addition to predicting maximum tree height in an environment, our framework can be extended to predict how other tree traits, such as stomatal density, depend on these resource constraints. Furthermore, it offers predictions for the relationship between height and whole canopy albedo, which is important for understanding the Earth's radiative budget, a critical component of the climate system. Because our model focuses on dominant features, which are represented by a small set of mechanisms, it can be easily integrated into more complicated ecological or climate models.
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pone.0020551
