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    Author(s): S. C. Stark; V. Leitold; J. L. Wu; M. O. Hunter; C. V. de Castilho; F. R. C. Costa; S. M. McMahon; G. G. Parker; M. Takako Shimabukuro; M. A. Lefsky; M. Keller; L. F. Alves; J. Schietti; Y. E. Shimabukuro; D. O. Brandao; T. K. Woodcock; N. Higuchi; P. B de Camargo; R. C. de Oliveira; S. R. Saleska
    Date: 2012
    Source: Ecology Letters 15:1406 - 1414.
    Publication Series: Scientific Journal (JRNL)
    Station: International Institute of Tropical Forestry
    PDF: Download Publication  (493.07 KB)


    Tropical forest structural variation across heterogeneous landscapes may control above-ground carbon dynamics. We tested the hypothesis that canopy structure (leaf area and light availability) – remotely estimated from LiDAR – control variation in above-ground coarse wood production (biomass growth). Using a statistical model, these factors predicted biomass growth across tree size classes in forest near Manaus, Brazil. The same statistical model, with no parameterisation change but driven by different observed canopy structure, predicted the higher productivity of a site 500 km east. Gap fraction and a metric of vegetation vertical extent and evenness also predicted biomass gains and losses for one-hectare plots. Despite significant site differences in canopy structure and carbon dynamics, the relation between biomass growth and light fell on a unifying curve. This supported our hypothesis, suggesting that knowledge of canopy structure can explain variation in biomass growth over tropical landscapes and improve understanding of ecosystem function.

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    Stark, S. C.; Leitold, V.; Wu, J. L.; Hunter, M. O.; de Castilho, C. V.; Costa, F. R. C.; McMahon, S. M.; Parker, G. G.; Takako Shimabukuro, M.; Lefsky, M. A.; Keller, M.; Alves, L. F.; Schietti, J.; Shimabukuro, Y. E.; Brandao, D. O.; Woodcock, T. K.; Higuchi, N.; de Camargo, P. B; de Oliveira, R. C.; Saleska, S. R. 2012. Amazon forest carbon dynamics predicted by profiles of canopy leaf area and light environment. Ecology Letters 15:1406 - 1414.


    Biomass growth, carbon balance, gap fraction, leaf area profiles, remote sensing of canopy structure, LiDAR

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