Response of litter decomposition and the soil environment to one-year nitrogen addition in a Schrenk spruce forest in the Tianshan Mountains, China

Human activities have increased the input of nitrogen (N) to forest ecosystems and have greatly affected litter decomposition and the soil environment. But differences in forests with different nitrogen deposition backgrounds. To better understand the response of litter decomposition and soil environment of N-limited forest to nitrogen deposition. We established an in situ experiment to simulate the effects of N deposition on soil and litter ecosystem processes in a Picea schrenkiana forest in the Tianshan Mountains, China. This study included four N treatments: control (no N addition), low N addition (LN: 5 kg N ha−1 a−1), medium N addition (MN: 10 kg N ha−1 a−1) and high N addition (HN: 20 kg N ha−1 a−1). Our results showed that N addition had a significant effect on litter decomposition and the soil environment. Litter mass loss in the LN treatment and in the MN treatment was significantly higher than that in the control treatment. In contrast, the amount of litter lost in the HN treatment was significantly lower than the other treatments. N application inhibited the degradation of lignin but promoted the breakdown of cellulose. The carbon (C), N, and phosphorus (P) contents of litter did not differ significantly among the treatments, but LN promoted the release of C and P. Our results also showed that soil pH decreased with increasing nitrogen application rates, while soil enzyme activity showed the opposite trend. In addition, the results of redundancy analysis (RDA) and correlation analyses showed that the soil environment was closely related to litter decomposition. Soil enzymes had a positive effect on litter decomposition rates, and N addition amplified these correlations. Our study confirmed that N application had effects on litter decomposition and the soil environment in a N-limited P. schrenkiana forest. LN had a strong positive effect on litter decomposition and the soil environment, while HN was significantly negative. Therefore, increased N deposition may have a negative effect on material cycling of similar forest ecosystems in the near future.

Adaptability evaluation of TRMM over the Tianshan Mountains in central Asia

Accurate precipitation in mountain area is very important for evaluating the hydrological process and ecological problem. With the satellite data having been widely used in the past few decades, adaptability evaluation becomes the principle problem. The adaptability of TRMM 3B43 in mountain area of Central Asia was analyzed in this study. The TRMM product was compared with the observed data for the period of 2000-2006. Four statistic parameters were introduced based on the statistical analysis theory. The results show that the bias reached -13.93% over the entire regions, and the correlation coefficients over 70% of stations were greater than 0.70. According to the accuracy analysis of TRMM, we found the errors have significant differences in time and space. On the whole, the precision in the warm seasons is much higher than that in the cold seasons. The precision of the southern and eastern areas is higher than the other areas in space. Additionally, the accuracy of TRMM with elevation was acceptable at very significant level. This study indicates that the precipitation from TRMM 3B43 could be applied in the Tianshan Mountains in Central Asia. It could provide reference for the use of new data source in the mountain area.

Potentially Toxic Elements in Oasis Agricultural Soils Caused by High-Intensity Exploitation in the Piedmont Zone of the Tianshan Mountains, China

Considering the pollution of potentially toxic elements (PTEs) in the soils of China, the present study analyzed the current state and influencing factors of PTEs in oasis soils using the model of absolute principal component score–multiple linear regression in the piedmont zone of the Tianshan Mountains. The possible non-carcinogenic and carcinogenic risks of PTEs at current concentrations were also explored using a human-health risk-assessment model. The results suggested that the extent to which potentially toxic elements in the soils of different geographical units in the study area is affected by human activities varies considerably. The PTEs Cd and As in the soils of the Yili River Watershed were the most strongly influenced by human activities, reaching levels of 40% and 59%, respectively. However, in the Bortala River Watershed, Cu, Cd, and As were the most strongly influenced by human activities, reaching levels of 33%, 64%, and 76%, respectively. Geographical units with a high degree of economic development (e.g., the Yili River Watershed) had, in contrast, low levels of PTE pollution caused by human activities, which may be related to the regional economic development structure. The human health risk assessment showed that the non-carcinogenic and carcinogenic risks of PTEs are currently below the threshold. However, increasing the arsenic content to 1.78 times the current level in the Bortala River Watershed would lead to carcinogenic risk. For the Yili River Watershed, a 3.33-fold increase in the arsenic content above its current level would lead to a carcinogenic risk. This risk should be addressed, and targeted environmental-protection measures should be formulated. The present research results will provide important decision support for regional environmental protection.

Differentiation and Variability in the Rhizosphere and Endosphere Microbiomes of Healthy and Diseased Cotton (Gossypium sp.)

The plant microbiome is a key determinant of health and productivity. However, it is still difficult to understand the structural composition of the bacterial and fungal microbiomes of diseased and healthy plants, especially the spatial dynamics and phylogenies of endophytic and rhizosphere microbial communities. We studied the differentiation and variability in the rhizosphere and endosphere microbiomes of healthy and diseased cotton from north and south of the Tianshan Mountains using the methods of PCR-based high-throughput sequencing and real-time quantitative PCR. The endophytic and rhizosphere bacterial abundances in the diseased plants were greater than those of healthy plants. The numbers of endophytic and rhizosphere fungi associated with diseased plants were greater than those associated healthy plants (p < 0.05). Endophytic and rhizosphere bacteria did not share common OTUs. The dominant rhizosphere bacteria were Proteobacteria (29.70%), Acidobacteria (23.14%), Gemmatimonadetes (15.17%), Actinobacteria (8.31%), Chloroflexi (7.99%), and Bacteroidetes (5.15%). The dominant rhizosphere fungi were Ascomycota (83.52%), Mortierellomycota (7.67%), Basidiomycota (2.13%), Chytridiomycota (0.39%), and Olpidiomycota (0.08%). The distribution of dominant bacteria in different cotton rhizosphere soils and roots differed, with the dominant bacteria Pseudomonas (15.54%) and Pantoea (9.19%), and the dominant fungi Alternaria (16.15%) and Cephalotrichum (9.10%) being present in the greatest numbers. At sampling points in different ecological regions, the total numbers of cotton endophytic and rhizosphere microbiome OTUs from southern to northern Xinjiang showed an increasing trend. There were significant differences in the composition and diversity of rhizosphere microbes and endophytes during the entire cotton growth period and in representative ecological regions (p < 0.01), whereas rhizosphere microbes and endophytes showed no significant differences among the four growth periods and in representative ecological regions. RB41, H16, Nitrospira, and Sphingomonas play important roles in the microbial ecology of cotton rhizosphere soil. Pseudomonas accounted for a large proportion of the microbes in the cotton rhizosphere soil. This study provides an in-depth understanding of the complex microbial composition and diversity associated with cotton north and south of the Tianshan Mountains.

Performance of RegCM4.5 in Simulating the Regional Climate of Western Tianshan Mountains in Xinjiang, China

The western Tianshan Mountains region in China has a complex topography where basins, mountains and glaciers co-exist. It is of great significance to study the sensitivity of meteorological factors in this region to different parameterization schemes of climate models. In this paper, the regional climate model RegCM4.5 is used to simulate the meteorological factor (mean temperature, maximum temperature, minimum temperature, precipitation and wind speed) occurring in the western Tianshan Mountains region from 2012 to 2016, so as to investigate the effects of different cumulus convective schemes (Grell, Tiedtke and Emanuel), including land cumulus convective schemes (LCCs) and ocean convective schemes (OCCs) on annual and seasonal simulations of meteorological factor by using the schemes of RUN1 (Grell for LCC and Tiedtke for OCC), RUN2 (Tiedtke for LCC and Emanuel for OCC), RUN3 (Grell for LCC and Emanuel for OCC) and ENS (the ensemble of RUN1, RUN2 and RUN3). The results show that the simulations of annual and seasonal meteorological factors are not significantly sensitive to the combination of LCCs and OCCs. In the annual simulations, RUN2 scheme has the best simulation performance for the maximum, average and minimum temperatures. However, other schemes of precipitation simulation outperform RUN2 scheme, and there is no difference among the four schemes for wind speed simulation. In the seasonal simulations, RUN2 scheme still performs well in the simulation of the average, maximum and minimum temperatures for four seasons, except for the simulation of the average temperature in spring and summer. For the simulation of the maximum temperature in summer, RUN2 scheme performs the same as ENS. For the simulation of other seasons, different meteorological factors have different performances in four seasons. Overall, the results show that different combinations of cumulus convection schemes can improve the simulation performance of meteorological factors in the western Tianshan Mountains of Xinjiang.