Tight coupling between leaf area index and foliage N content in arctic plant communities

Author Posting. © The Authors, 2004. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Oecologia 142 (2005): 421-427, doi:10.1007/s00442-004-1733-x. The large spatial heterogene...

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Bibliographic Details
Published in:Oecologia
Main Authors: van Wijk, Mark T., Williams, Mathew, Shaver, Gaius R.
Format: Report
Language:English
Published: 2004
Subjects:
Online Access:https://hdl.handle.net/1912/789
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Summary:Author Posting. © The Authors, 2004. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Oecologia 142 (2005): 421-427, doi:10.1007/s00442-004-1733-x. The large spatial heterogeneity of arctic landscapes complicates efforts to quantify key processes of these ecosystems, for example productivity, at the landscape level. Robust relationships that help to simplify and explain observed patterns, are thus powerful tools for understanding and predicting vegetation distribution and dynamics. Here we present the same linear relationship between leaf area index and total foliar nitrogen, the two factors determining the photosynthetic capacity of vegetation, across a wide range of tundra vegetation types in both Northern-Sweden and Alaska between leaf area indices of 0 and 1 m2 m-2, which is essentially the entire range of leaf area index values for the Arctic as a whole. Surprisingly, this simple relationship arises as an emergent property at the plant community level, whereas at the species level a large variability in leaf traits exists. As the relationship between LAI and foliar N exists among such varied ecosystems, the arctic environment must impose tight constraints on vegetation canopy development. This relationship simplifies the quantification of vegetation productivity of arctic vegetation types as the two most important drivers of productivity can now be estimated reliably from remotely sensed NDVI images. This work was funded by the US National Science Foundation.