Assessment of the species composition of forest floor horizons in mixed spruce-beech stands by Near Infrared Reflectance Spectroscopy (NIRS)

How the mixture of tree species modifies short-term decomposition has been well documented using litterbag studies. However, how litter of different tree species interact in the long-term is obscured by our inability to visually recognize the species identity of residual decomposition products in the two most decomposed layers of the forest floor (i.e. the Oe and Oa layers respectively). To overcome this problem, we used Near Infrared Reflectance Spectroscopy (NIRS) to determine indirectly the species composition of forest floor layers. For this purpose, controlled mixtures of increasing complexity comprising beech and spruce foliage materials at various stages of decomposition from sites differing in soil acid base status were created. In addition to the controlled mixtures, natural mixtures of litterfall from mixed stands were used to develop prediction models. Following a calibration/validation procedure, the best regression models to predict the actual species proportion from spectral properties were selected for each tree species based on the highest coefficient of determination (R-2) and the lowest root mean square error of prediction (RMSEP). For the validation, the R-2 (predictions versus true proportions) were 0.95 and 0.94 for both beech and spruce components in mixtures of materials at all stages of decomposition from the gradient of sites. The R-2 decreased only marginally by 0.04 when models were tested on independent samples of similar composition. The best models were used to predict the beech-spruce proportion in Oe and Oa layers of unknown composition. They provided in most cases plausible predictions when compared to the composition of the canopy above the sampling points. Thus, tedious and potentially erroneous hand sorting of forest floor layers may be replaced by the use of NIRS models to determine species composition, even at late stages of decomposition. (c) 2010 Elsevier Ltd. All rights reserved.