Large-scale climatic and geophysical controls on the leaf economics spectrum

Proc Natl Acad Sci U S A. 2016 Jul 12;113(28):E4043-51. doi: 10.1073/pnas.1604863113. Epub 2016 Jun 27.

Abstract

Leaf economics spectrum (LES) theory suggests a universal trade-off between resource acquisition and storage strategies in plants, expressed in relationships between foliar nitrogen (N) and phosphorus (P), leaf mass per area (LMA), and photosynthesis. However, how environmental conditions mediate LES trait interrelationships, particularly at large biospheric scales, remains unknown because of a lack of spatially explicit data, which ultimately limits our understanding of ecosystem processes, such as primary productivity and biogeochemical cycles. We used airborne imaging spectroscopy and geospatial modeling to generate, to our knowledge, the first biospheric maps of LES traits, here centered on 76 million ha of Andean and Amazonian forest, to assess climatic and geophysical determinants of LES traits and their interrelationships. Elevation and substrate were codominant drivers of leaf trait distributions. Multiple additional climatic and geophysical factors were secondary determinants of plant traits. Anticorrelations between N and LMA followed general LES theory, but topo-edaphic conditions strongly mediated and, at times, eliminated this classic relationship. We found no evidence for simple P-LMA or N-P trade-offs in forest canopies; rather, we mapped a continuum of N-P-LMA interactions that are sensitive to elevation and temperature. Our results reveal nested climatic and geophysical filtering of LES traits and their interrelationships, with important implications for predictions of forest productivity and acclimation to rapid climate change.

Keywords: Amazon basin; functional biogeography; leaf traits; plant traits; tropical forests.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Altitude
  • Climate*
  • Forests*
  • Geography
  • Peru
  • Plant Leaves / growth & development*
  • Plant Leaves / metabolism
  • Remote Sensing Technology*