Salinity effects on microbial communities in saline soils is still unclear, and little is known about subsurface soil microbial communities especially in saline or hypersaline ecosystems. Here we presented the survey of the prokaryotic community in saline soils along a salinity gradient (17.3–148.3 dS/m) in surface (0–10 cm) and subsurface (15–30 cm) saline soils of Qarhan Salt Lake, China. Moreover, we compared them with three paired nonsaline normal soils. Using the high-throughput sequencing technology and several statistical methods, we observed no significant community difference between surface soils and subsurface soils. For environmental factors, we found that TOC was the primary driver of the prokaryotic community distribution in surface saline soils, so was pH in subsurface saline soils. Salinity had more effects on the prokaryotic community in subsurface saline soils than in surface saline soils and played a less important role in saline soils than in saline waters or saline sediments. Our research provided references for the prokaryotic community distribution along a salinity gradient in both surface and subsurface saline soils of arid playa areas.
High-throughput amplicon sequencing technology has been widely used in soil microbiome studies. Here, we estimated the bias of amplicon sequencing data affected by DNA extraction methods in a saline soil, and a non-saline normal soil was used as a control. Compared with the normal soil, several unique points were observed in the saline soil. The soil washing pretreatment can improve not only DNA quantity and quality but also microbial diversities in the saline soil; therefore, we recommend the soil washing pretreatment for saline soils especially hypersaline soils that cannot be achieved with detectable DNA amounts without the pretreatment. Also, evenness indices were more easily affected by DNA extraction methods than richness indices in the saline soil. Moreover, proportions of Gram-positive bacteria had significant positive correlations with the achieved microbial diversities within replicates of the saline soil. Though DNA extraction methods can bias the microbial diversity or community and relative abundances of some phyla/classes can vary by a factor of more than five, soil types were still the most important factor of the whole community. We confirmed good comparability in the whole community, but more attention should be paid when concentrating on an exact diversity value or the exact relative abundance of a certain taxon. Our study can provide references for the DNA extraction from saline and non-saline soils and comparing sequencing data across studies who may employ different DNA extraction methods.
The Tibetan antelope (chiru, Pantholops hodgsoni) is one of the most endangered mammals native to the Qinghai-Tibetan Plateau. The population size has rapidly declined over the last century due to illegal hunting and habitat damage. In the past 10 years, the population has reportedly been expanding due to conservation efforts. Several lines of evidence suggest that the Tibetan antelope has undergone a demographic bottleneck. However, the consequences of the bottleneck on genetic diversity and the post-bottleneck genetic recovery remain unknown. In this study, we investigate the genetic variation of 15 microsatellite loci from two Tibetan antelope populations sampled in 2003 (Pop2003) and 2013 (Pop2013). A higher level of genetic diversity (NA, 13.286; He, 0.840; PIC, 0.813; I, 2.114) was detected in Pop2013, compared to Pop2003 (NA, 12.929; He, 0.818; PIC, 0.789; I, 2.033). We observe that despite passing through the bottleneck, the Tibetan antelope retains high levels of genetic diversity. Furthermore, our results show significant or near significant increases in genetic diversity (He, PIC and I) in Pop2013 compared with Pop2003, which suggests that protection efforts did not arrive too late for the Tibetan antelope.
A data fusion method for the estimation of residential radon level distribution in any Pennsylvania county is proposed. The method is based on a multisample density ratio model with variable tilts and is applied to combined radon data from a reference county of interest and its neighboring counties. Beaver county and its four immediate neighbors are taken as a case in point. The distribution of radon concentration is estimated in each of six periods, and then the analysis is repeated combining the data from all the periods to obtain estimates of Beaver threshold probabilities and the corresponding confidence intervals.
Fitting parametric models or the use of the empirical cumulative distribution function are problematic when it comes to the estimation of tail probabilities from small samples. A possible remedy is to fuse or combine the small samples with additional data from external sources and base the inference on the so called density ratio model with variable tilt functions, which widens the support of the estimated distribution of interest. This approach is illustrated using residential radon concentration data collected from western Pennsylvania.
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