Effects of mutual shading of tree crowns on prediction of photosynthetic light-use efficiency in a coastal Douglas-fir forest

• Published in: Tree physiology Vol. 28; no. 6; pp. 825 - 834
• Main Authors: Hilker, T, Coops, N.C, Schwalm, C.R, Jassal, R.S, Black, T.A, Krishnan, P
• Format: Journal Article
• Language: English
• Published: Canada 01.06.2008
• Subjects: age structure; Canada; canopy; Darkness; eddy covariance; equations; estimation; forest trees; lidar; Light; maritime climate; Models, Biological; photosynthesis; Photosynthesis - physiology; photosynthetically active radiation; plant competition; primary productivity; Pseudotsuga - physiology; Pseudotsuga menziesii; radiation use efficiency; regression analysis; remote sensing; shade; solar radiation; spatial data; stand structure; Sunlight; tree crown; British Columbia; Canada
• ISSN: 0829-318X; 1758-4469
• DOI: 10.1093/treephys/28.6.825

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.