RESUMO
Understanding the spatial heterogeneity of soil available medium- and micro-elements in karst area can provide a valuable theoretical guideline for soil nutrient management of karst ecosystem. We collected soil samples at a soil depth of 0-10 cm using grid sampling (20 m×20 m) in a 25 hm2 (500 m×500 m) dynamic monitoring plot. We further analyzed the spatial variability of soil medium- and micro-elements and their drivers, with classic statistics analysis and geo-statistics analysis. The results showed that the average contents of exchangeable Ca and Mg and available Fe, Mn, Cu, Zn, and B were 7870, 1490, 30.24, 149.12, 1.77, 13.54, and 0.65 mg·kg-1, respectively. The coefficient of variation of the nutrients ranged from 34.5% to 68.8%, showing a medium degree of their spatial variation. The coefficient of determination of the best-fit semi-variogram models of each nutrient was higher than 0.90, except for available Zn (0.78), indicating a strong predictive power for the spatial variation of the nutrients. The nugget coefficients for all the nutrients were less than 50%, showing a moderate spatial correlation, and the structural factors played a pivotal role. The spatially autocorrelated variation was within the range of 60.3-485.1 m, among which available Zn showed the lowest range and the deepest fragmentation degree. The spatial distribution of exchangeable Ca, Mg, and available B were consistent, with contents in the depression being significantly lower than that in other habitats. The contents of available Fe, Mn, and Cu declined with the increases of altitude and were significantly lower on the hilltop than in other habitats. The spatial variation of soil medium- and micro-elements was closely related to topographic factors in karst forest. Elevation, slope, soil thickness, and rock exposure rate were the primary drivers of spatial variation of soil elements and need to be considered in soil nutrient management of karst forestlands.
Assuntos
Ecossistema , Solo , Solo/química , Florestas , ChinaRESUMO
Tree mortality is an important ecological process in forests. It is crucial to understand how tree mortality affects spatial patterns and interspecific associations for revealing the mechanisms of tree mortality and community dynamics. We employed the correlation method of spatial point pattern analysis to analyse the variations in spatial patterns and interspecific relations before and after mortality using data obtained from two surveys of a 25 hm2 plot in the Mulun National Natural Reserve, China. The results showed that most species had an aggregated distribution both pre- and post-mortality. The proportion of species with aggregated distribution reduced slightly post-mortality compared with that for pre-mortality. Increases in the number of species with random distribution at small scale indicated that tree death was not random. At the species level, there were significant positive associations between dead and live trees of the 13 common species at different levels of 0-30 m range, suggesting weak intraspecific and interspecific competition among dominant species. Pre- and post-mortality interspecific associations were mostly positive, which remained stable during the period of two surveys for most species, indicating that the community had reached a rather stable stage. Following tree mortality, the number of species with positive associations increased at 1-30 m scales, whereas the number of species with negative and no associations decreased at most scales. These results indicated that the pressure of interspecific competition was relieved to some extent after individual death.
Assuntos
Ecossistema , Árvores , Humanos , Florestas , China , Análise EspacialRESUMO
Soil samples were collected from the depression (200 m x 100 m) in Karst area's Mulun National Nature Reserve by grid sampling method (20 m x 20 m), with the spatial heterogeneity of soil nutrients analyzed by the methods of classic statistics and geo-statistics. The soil pH showed small variation, while the soil nutrients showed moderate variation, being in the order of available phosphorus (AP) > available potassium (AK) > available nitrogen (AN) > organic matter (OM) > total potassium (TK) > total phosphorus (TP) > total nitrogen (TN). Spherical model fitted best for soil pH, exponential model fitted best for soil TK and AK, and Gaussian model fitted best for other variables. The variation range of soil pH and AK was smaller, being 58.1 m and 41.1 m, respectively, that of soil OM, TN, TP, AN, and AP was from 100 m to 150 m, and that of soil TK was the largest (463.5 m). Soil TK and TN showed moderate spatial autocorrelation, while other soil nutrients showed strong spatial autocorrelation. Soil pH and AK presented fragmented patch distribution, showing high heterogeneity, while soil OM, TP, and TK changed gently, high in the middle and low in two sides. The spatial patterns of soil AN and AP were similar, i.e., increased with landform slope. Soil TN had a distribution pattern of low in the middle and high in two sides. Vegetation, topography, and high heterogeneity of micro-habitat were the main factors caused the differences of the spatial distribution patterns of soil nutrients in the Reserve.
