Environmental Correlates of Distribution of the 25 Broad-leaved Tree Species Indigenous to Guangdong Province,China

Twenty-five tree species indigenous to Guangdong Province were chosen in this study to portray their distribution patterns in relation to environmental factors. Both data of species distribution and environmental factors were tabulated based on a digitized map of Guangdong Province gridded at 0.5° latitude × 0.5° longitude. Grid-based diversity was mapped using DMAP, a distribution mapping program, and horizontal patterns were assessed using Kruskal-Wallis tests. The diversity center of the indige- nous tree species under study is located north of 23° N. These tree species exhibit significant latitudinal variation (P = 0.007 4), but no significant longitudinal difference (P = 0.052 2). Non-metric Multidimensional Scaling (NMS) identified five different ecological species groups, while Canonical Correspondence Analysis (CCA) showed the distribution of tree species along each of the five envi- ronmental gradients. An understanding of the environmental correlates of distribution patterns has great implication for the introduc- tion of the indigenous tree species for afforestation.

Geospatial Mapping of Soil Nitrate-Nitrogen Distribution Under a Mixed-Land Use System

Mapping the spatial distribution of soil nitrate-nitrogen （NO3=N） is important to guide nitrogen application as well as to assess environmental risk of NO3-N leaching into the groundwater. We employed univariate and hybrid geostatistical methods to map the spatial distribution of soil NO3-N across a landscape in northeast Florida. Soil samples were collected from four depth increments （0-30, 30-60, 60-120 and 120-180 cm） from 147 sampling locations identified using a stratified random and nested sampling design based on soil, land use and elevation strata. Soil NO3-N distributions in the top two layers were spatially autocorrelated and mapped using lognormal kriging. Environmental correlation models for NO3-N prediction were derived using linear and non-linear regression methods, and employed to develop NO3-N trend maps. Land use and its related variables derived from satellite imagery were identified as important variables to predict NO3-N using environmental correlation models. While lognormal kriging produced smoothly varying maps, trend maps derived from environmental correlation models generated spatially heterogeneous maps. Trend maps were combined with ordinary kriging predictions of trend model residuals to develop regression kriging prediction maps, which gave the best NO3-N predictions. As land use and remotely sensed data are readily available and have much finer spatial resolution compared to field sampled soils, our findings suggested the efficacy of environmental correlation models based on land use and remotely sensed data for landscape scale mapping of soil NO3-N. The methodologies implemented are transferable for mapping of soil NO3-N in other landscapes.

Distribution of Bacterial Communities in Petroleum-Contaminated Soils from the Dagang Oilfield, China

Diversity in bacterial communities was investigated along a petroleum hydrocarbon content gradient(0-0.4043 g/g)in surface(5-10 cm)and subsurface(35-40 cm)petroleum-contaminated soil samples from the Dagang Oilfield,China.Using 16S rRNA Illumina high-throughput sequencing technology and several statistical methods,the bacterial diversity of the soil was studied.Subsequently,the environmental parameters were measured to analyze its relationship with the community variation.Nonmetric multidimensional scaling and analysis of similarities indicated a significant difference in the structure of the bacterial community between the nonpetroleum-contaminated surface and subsurface soils,but no differences were observed in different depths of petroleum-contaminated soil.Meanwhile,many significant correlations were obtained between diversity in soil bacterial community and physicochemical properties.Total petroleum hydrocarbon,total organic carbon,and total nitrogen were the three important factors that had the greatest impacts on the bacterial community distribution in the long-term petroleum-contaminated soils.Our research has provided references for the bacterial community distribution along a petroleum gradient in both surface and subsurface petroleum-contaminated soils of oilfield areas.

Impact of environmental factors on water quality at multiple spatial scales and its spatial variation in Huai River Basin, China

Quantitative assessment of water quality and its spatial variation identification, as well as the discernment of primary factors affecting water quality are in its urgent in water environment management. In this study, four key water quality indicators,namely, ammonia nitrogen(NH_4^+-N), permanganate index(COD_(Mn)), total phosphorus(TP) and total nitrogen(TN) at 71 sampling sites were selected to evaluate water quality and its spatial variation identification. More concerns were emphasized on the anthropogenic factors(land use pattern) and natural factors(river density, elevation and precipitation) to quantify the overall water quality variations at different spatial scales. Results showed that the Yi-Shu-Si River sub-basin had a better water quality status than the Huai River sub-basin. The moderate polluted area nearly distributed in the upper and middle reaches of the Shaying River and Guo River. The high cluster centers which were surrounded with COD_(Mn), NH_4^+-N, TN and TP mainly also distributed in the upper and middle reaches of the Shaying River and Guo River. Redundancy analysis showed that the 200 m buffer area acted as the most sensitive area, which was easily subjected to pollution. The precipitation was identified as the most important variables among all the studied hydrological units, followed by farmland, urban land or elevation. The point source pollution was still existed although the non-point source pollution was also identified. The urban surface runoff pollution was severer than farmland fertilizer loss at the sub-basin scale in flood season, while the farmland showed "small-scale" effects for explaining overall water quality variations. This research is helpful for identifying the overall water quality variations from the scale-process interactions and providing a scientific basis for pollution control and decision making for the Huai River Basin.