Question: Do Beer's Law models, multi-layer scattering models, and a semi-empirical model for predicting PAR transmission through understorey vegetation give comparable results? Do different driving variables (LAI, PLAI and percentage cover) give different results? How do the models vary when fit with species-specific, species-average and the ‘default’ parameters recommended in the literature?
Location: Upland boreal forests of western North America.
Methods: In calibration and validation plots, PAR transmission was measured, total cover visually estimated, and leaf dispersion, PLAI and cover estimated for each species using a point-frame. Leaf inclination was measured by clinometer. PAR transmission was modelled using empirically-fit Beer's Law models, a semi-empirical model based on hemispherical gap fraction and first-order scattering, and a multi-layer model allowing multiple scattering. All models were modified to use leaf area index (LAI), vertically projected leaf area index (PLAI), or percentage cover data.
Results: The empirical Beer's Law models had the least bias and best precision in predicting PAR transmission. The semi-empirical model also had little bias and good precision, since the scattering coefficient compensated for problems in the estimation of gap fraction. The multi-layer model consistently underestimated transmission. There was little benefit in accounting for species separately. LAI and PLAI-based models were the most precise, but percentage cover models also provided reasonable predictions of PAR transmission.
Conclusions: PAR transmission through forest understories can be simply modelled with Beer's Law using one empirical coefficient representing the average understorey species. More complex scattering models are less effective, likely because they fail to account for the complexity of the dispersion of this vegetation layer and its effect on radiation scattering.
Abbreviations: PAR = Photosynthetically active radiation (400–700 nm); LAI = Leaf area index; PLAI = Vertically projected leaf area index.