Effects of mutual shading of tree crowns on prediction of photosynthetic light-use efficiency in a coastal Douglas-fir forest
Thomas Hilker (1, 2), Nicholas C. Coops (1), Christopher R. Schwalm (3), Rachhpal (Paul) S. Jassal (4), T. Andrew Black (4) and Praveena Krishnan (4)
1. Faculty of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada / 2. Corresponding author () / 3. Department of Natural Resources, University of Minnesota, St. Paul, MN 55155, USA / 4. Faculty of Land and Food Systems, University of British Columbia, 2357 Main Mall, Vancouver, BC V6T 1Z4, Canada / Received April 16, 2007; accepted November 29, 2007; published online April 1, 2008
Summary
Gross primary production (GPP) is often expressed as the product of absorbed photosynthetically active radiation and the efficiency
(ε) with which a plant community uses absorbed radiation in biomass production. Light-use efficiency is affected by environmental
stresses, and varies diurnally and seasonally. Uncertainty about ε can be a serious limitation when modeling GPP. An important
determinant of ε is the amount and type of solar radiation incident on a canopy, because an abundance of light can trigger
a photo-protective reaction, diminishing GPP. The radiation regime in a forest canopy is determined by the predominant sky
conditions and by mutual shading of tree crowns. Shading effects, producing shifts in the amount of incident direct and diffuse
solar radiation, have been largely ignored, however, because they depend on forest structure and are difficult to measure.
We describe a new approach for estimating changes in mutual canopy shading throughout the day and year based on a canopy structure
model derived from light detection and ranging (LiDAR). Proportions of canopy shading were then combined with eddy covariance
data to assess the explanatory power for variance in ε by regression tree analysis over half-hourly, daily and weekly time
scales. The approach explained between 75 and 97% of variance in ε, representing an increase of between 5 and 16% compared
with models driven solely by meteorological variables.
