The mountaineous rain forest in south Ecuador has developed on poor and acid soils, with low nutrient availability. The additional ferilization resulting from anthropogenic biomass burning constitutes a significant disturbance of this ecosystem, its functioning and biodiversity. Thus it is planned to employ isotope analyses for quantifying the pathways. of nitrate and sulfate deposition in these natural forests.
The spatial and temporal variability of the nitrate (NO 3 − ) concentration in seepage water below the main rooting zone of a mature spruce forest was investigated using 121 suction cups which were implemented in a 2×2 m grid at 40 cm depth in the mineral soil. Seepage was collected at least monthly during the vegetation period in 2005. In January 2006 a clear cut was performed on the plot and monthly seepage water was collected with 118 suction cups. Conventional and geostatistical methods were used to investigate the spatial and temporal variability in NO 3 − . We tried to explain the observed variability with multiple classification analysis (MCA) and multiple linear regression models (MLR) based on vegetation, stand and soil parameters, which could be measured without disturbance of the ecosystem. The NO 3 − concentration in the mature stand reached mean values up to 35.2 mg l −1 per sampling date and the distributions were positively skewed. The temporal variability was much lower than the spatial variability. NO 3 − in seepage water showed clear structural patterns over the whole vegetation period. Spatial autocorrelation ranged between 16 and 19 m and structural variance was between 65% and 80% of the whole model semivariance. However, for practical purposes it should be sufficient to consider an autocorrelation range of about 12 m as the last 5-6.5 m only explained 5% of the total structural variance of NO 3 − in seepage water. Vegetation and stand parameters such as distance and size of the trees surrounding the measuring points explained about 40% of total variability in the MCA and MLR models. After clear cut, concentration means per sampling date were below 10 mg l −1 in spring but increased to more then 150 mg l −1 until December 2006. The distribution was nearly normal. The patterns of spatial and temporal variability were reversed compared to the mature stand. Structuring was less pronounced with autocorrelation ranges decreasing from 10 m in April 2006 to values below 3 m during the following vegetation period. The structuring variance was below 30% at most sampling dates. Thickness of the humus layer and amount of water in the sampling bottles, which were not considered in the mature stand, contributed significantly to the explanation of variability after the clear cut. Together with vegetation coverage and stand parameters 30% of the total NO 3 − variability could be explained in the MCA and MLR models. The consequences for future seepage water investigations are discussed.
Soil science lacks a fine spatial resolution imaging technique that is able to measure the quantity and quality of organic matter (OM) for complete soil profiles. We tested whether laboratory Vis-NIR imaging spectroscopy, together with an unsupervised k-means classification, can be used to distinguish between different OM fractions in a Histosol profile. A rectangular soil column (22-cm long) of a folic Histosol (Tangelhumus) was collected from an alpine Norway spruce forest in south-eastern Germany with a stainless steel box (100 × 100 × 300 mm). A hyperspectral camera (400-1000 nm with 160 bands) with a pixel sampling of 63 × 63 μm was used to acquire the data. We took images of three vertical cuts through the soil profile, each separated laterally by 25 mm. Reference samples were taken at representative locations and analysed for soil organic matter (SOM) quantity and quality with a CN elemental analyser and solid-state 13 C nuclear magnetic resonance (NMR) spectroscopy. Principal component analysis and unsupervised k-means classifications were used to discriminate between different qualities of OM. We identified three OM fractions based on their reflectance characteristics: living and dead roots with a small degree of decomposition, decomposed particulate OM and decomposed amorphous OM. These fractions were consistent with the morpho-functional classes of two soil classification systems and can be used for the improved identification of diagnostic horizons. The spectra of the fractions contained additional information on, for example, lignin content and the degree of decomposition. Vis-NIR imaging spectroscopy is a powerful technique for mapping SOM quality in visually homogeneous organic surface layers.
Soil net nitrogen (N) mineralization and nitrification as well as gross nitrification rates were studied in a forest soil within a 30×18m homogeneous plot located in an N saturated mature spruce stand at the Höglwald Forest (Bavaria, Germany) in order to explain the small-scale variation in nitrate (NO 3 − ) concentration in seepage water. Seepage water was sampled below the main rooting zone in 40cm depth with suction cups over two periods at 20 measuring spots respectively. The sampling spots were uniformly distributed over the plot for both sampling periods, and represented the whole concentration range of seepage water NO 3 − concentrations measured within a close mesh of 121 suction cups. At each measuring spot soil net N mineralization, gross and net nitrification, heterotrophic soil respiration, extractable soil ammonium (NH 4 + ) and NO 3 − , and additional physical and chemical soil parameters were measured in the organic layer and correlated with the NO 3 − concentrations in seepage water. Furthermore, the effects of environmental parameters on N conversion processes were evaluated using multiple linear regression analysis. We found that the small-scaled variations in seepage water NO 3 − concentration were related to similar small-scaled variations in key processes of microbial N turnover rates in the organic layer. Within this study net N mineralization in the organic layer could explain 51-59% of the corresponding small-scale variation of nitrate concentrations in seepage water below the main rooting zone using a multiple linear regression model with stepwise procedure. In addition, we found that small-scale patterns of N turnover in the organic layer were strongly influenced by water content in the organic layer and the dry mass of organic matter.
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