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Altitude can drive the distribution of living organisms through effects on both soil properties and plant-fungi interactions. We examined how altitude influenced soil fungal communities (including three ecological groups), directly and indirectly through variation in soil properties and/or plant communities, by sampling along an altitudinal gradient from 1300 to 4200 m a.s.l. in an alpine meadow in Western China. We found that soil properties (r = 0.401; P = 0.001) and plant communities (r = 0.332; P = 0.001) significantly affected soil fungal community composition. Soil nitrogen content, altitude and nitrate nitrogen content were the best predictors of saprophytic, symbiotic and biotrophic fungi, respectively. Altitude increased the relative abundance (standard coefficient = 0.385) and richness (standard coefficient = 0.689) of soil saprophytic fungi by promoting soil nitrogen content (standard coefficient = 0.574). Altitude directly increased soil symbiotic fungi (standard coefficient = 0.811), while nitrate nitrogen content promoted the richness and abundance of plant fungal pathogens. Our results shed light on drivers of soil fungi community composition and the distribution of different ecological groups along an altitudinal gradient in an alpine meadow.

While global demand for rare earth elements (REEs) is rapidly growing, recent studies have suggested that REEs are pollutants of emerging concern. In this study, the spatial distribution and risk assessments of REEs in the upland soils of northern China were comprehensively investigated. The total REE concentrations ranged from 81 to 180 mg/kg, with average concentrations of 123, 128, and 98.3 mg/kg in the northwestern, northern, and northeastern zones, respectively. The decreasing trend of REE contents from northwest to northeast might be influenced by variation in the REE metallogenic belt distribution, mining activities, and precipitation intensity in these regions. The ratio of light rare elements (LREEs) to heavy rare elements (HREEs) ranged from 5.04 to 9.06, revealing obvious fractionation between them in upland soils and indicating that LREEs enrichment was common in northern China. The significantly positive correlations between the REEs indicated that REEs might frequently coexist and share similar sources in the upland soils of northern China. Based on a modified ecological risk index (eRI), REEs were estimated to pose relatively low ecological risks to current environmental residues, with eRI values ranging from 0.564 to 0.984. Fortunately, the estimated daily intakes of REEs from soils for children (1.08-2.41 μg/kg/day) and adults (0.119-0.312 μg/kg/day) were well below the safety thresholds. However, the health risks posed by REEs in upland soils were estimated to be higher for children. Thus, the continuous monitoring of REE abundance in soils is essential to avoid potential health risks.

Gene order has been utilized as a phylogenetic signal for many taxa. However, its phylogenetic performance has not been evaluated in Nematoda. As there is only one nematode mitogenome available to date, in the Diplogasteridae family, we sequenced the mitogenome of Allodiplogaster sp. and constructed a phylogeny for Nematoda using this updated mitogenome dataset. We then compared this phylogeny to one constructed using gene order. The complete mitochondrial genome of Allodiplogaster sp. was 13,953 bp in size and included 22 tRNAs, two rRNAs, and 12 protein-coding genes. To assess how Allodiplogaster sp. is related to other nematode species, we used Bayesian inference and maximum likelihood algorithms to construct phylogenetic trees of the Nematoda. We found that: 1) The target species Allodiplogaster sp. is closely related to Allodiplogaster sudhausi. The topology of the mitogenome based phylogeny was nearly identical to previous phylogenies created using 18S rRNA data, except for the placement of the Strongyloididae family. 2) The maximum likelihood tree constructed using gene order was roughly consistent with the mitogenome-based tree at the family level, but not at the species level. 3) Protein-coding genes were ordered differently in Allodiplogaster sp. versus Allodiplogaster sudhausi; this represents the first report of such a reordering in the class Chromadorea in our study. Our study confirms that gene order represents useful phylogenetic information for the Nematoda: the maximum likelihood tree based on gene order provided additional support for the nematode phylogeny constructed using molecular data.

The spatial dynamics of heavy metal contamination in the upland soils of northern China are relatively unknown, despite the region’s high contribution to the national grain output. In this study, the concentrations of As, Cd, Co, Cr, Cu, Mn, Pb, Sb, Sc, Ti, and Zn and subsequent ecological and human health risks were investigated in three major grain producing areas (Hexi Corridor, L1; Hetao irrigation area, L2; and eastern Inner Mongolia, L3) of northern China. Among the heavy metals, Ti had the highest average concentration of 3.02 g/kg, followed by Mn (470 mg/kg), Cr (56.6 mg/kg), Zn (34.3 mg/kg), Pb (19.4 mg/kg), Cu (17.8 mg/kg), Co (9.66 mg/kg), Sc (7.26 mg/kg), As (5.35 mg/kg), Sb (0.73 mg/kg), and Cd (0.17 mg/kg). Generally, the heavy metal concentrations decreased from west to east (L1 > L2 > L3) across northern China. Moreover, three potential sources of the heavy metal were distinguished, including natural process, anthropogenic activities (industrial development and agricultural cultivation), and atmospheric deposition. Although the contamination of the single metal (including Cd, Cr, Cu, and Pb) was moderate in L1 and L2, the combined contamination was low in the upland soils. It was noted that Cd posed a moderate to considerable ecological risk on the upland soils in northern China. This metal was the most sensitive factor in assessing the combined ecological risk, with a contribution rate of 91.56–94.84%. Considering the ingestion exposure, the current concentrations of the metals posed minimal risks to human health. Furthermore, children experienced higher health risks than adults. Present study analyzed the probabilistic distribution of contamination, ecological, and health risk of heavy metals in upland soils of northern China, providing fundamental information for better agricultural heavy metal pollution assessment in China.

Changes in land use and the conversion of natural forests to agricultural fields and cattle pastures are threatening the survival of many species of wild animals, including nonhuman primates. Given its almost 1.4 billion people, China faces a difficult challenge in balancing economic development, human well-being, environmental protection, and animal conservation. We examined the effects of poverty, anthropogenic land use (cropland and pasture/grazing), human population growth, government investment in science and public attention to primates during the period from the 1980s to 2015 on primate population persistence in China. We analyzed these data using generalized mixed-effects models, structural equation models (SEM) and random forests (a machine learning technique). We found that 16 of 21 (76%) primate species in China, for which data are available, have experienced a population decline over the past 35 years. Factors contributing most to primate population decline included human poverty and the conversion of natural habitat to cropland. In contrast, the five species of primates that were characterized by recent population increases were the subjects of substantial government research funding and their remaining distribution occurs principally in protected areas (PAs). We argue that increased funding for research, the establishment and expansion of PAs, a national policy focused on reducing poverty, and educational programs designed to inform and encourage local people to participate in scientific investigation and wildlife protection, can mitigate the negative impacts of historical patterns of land conversion on primate population survival in China.

Correlational models are widely used to predict changes in species' distribution, but generally have failed to address the comprehensive effects of anthropogenic activities, climate change, habitat connectivity and gene flow on wildlife sustainability. Here, we used integrated approaches (MAXENT model, circuit model and genetic analysis) to assess and predict the effects of climate change and anthropogenic activities on the distribution, habitat connectivity, and genetic diversity of an endangered primate, Rhinopithecus bieti, from 2000 to 2050. We created six scenarios: climatic factors only (scenario-a), anthropogenic activities only (scenario-b), climatic factors and anthropogenic activities (scenario-c), plus three additional scenarios that included climatic factors and anthropogenic activities but controlled for individual anthropogenic activities (scenario-d: grazing, scenario-e: collecting, and scenario-f: grazing and collecting). The results indicate that areas of suitable habitat for R. bieti are expected to decline by 8.0%–22.4% from 2000 to 2050, with the collection of local forest products and the grazing of domesticated cattle as the primary drivers of landscape fragmentation and range contraction. If these anthropogenic activities are strictly controlled, however, the area of suitable habitat is predicted to increase by10.4%–14.3%. We also found that habitats vulnerable to human disturbance were principally located in areas of low habitat connectivity resulting in limited migration opportunities and increased loss of genetic diversity among R. bieti living in these isolated subpopulations. Thus, we suggest that effective management policies to protect this species include prohibiting both livestock grazing and the collecting of forest products. Although our study focuses on a single primate species, the conservation modeling approaches we presented have wide applicability to a broad range of threatened mammalian and avian taxa that currently inhabit a limited geographic range and are affected by anthropogenic activities (e.g. collecting, grazing, hunting), loss of habitat connectivity, reduced genetic diversity, and the effects of climate change.

Extensive application of agricultural plastic films has resulted in abundant film residues in farmlands. Phthalate acid esters (PAEs) are vital additives of the agricultural plastic film and are easily emitted into soils. However, spatio-temporal variations of diffuse PAEs loss to water bodies have not been explored in China. This study used an integrated estimation framework and high-resolution activity data to conduct a comprehensive inventory of diffuse PAEs loss associated with plastic films of six main crop types in China for 1991-2017. We found that the diffuse PAEs loss induced from agricultural plastic films increased 10.57-46.30 kg over the same time. Di-butyl phthalate (DBP) and bis(2-ethylhexyl) phthalate (DEHP) accounted for ~75% of the national total loss. High PAEs loss regions are mainly located in Eastern China, the Middle-Lower Yangtze Plain, and eastern Yunnan and Sichuan provinces. We proved that PAEs emission, agricultural film residues, surface runoff, precipitation, and soil organic carbon explained 19.64%, 17.50%, 15.45%, 12.88%, and 9.83% of the total variation, respectively. The potential ecological risks to the various aquatic species were assessed to be low. Overall, our results are valuable for addressing severe agricultural plastic film residues and associated pollutant emissions and losses in China.

An understanding of primate movement patterns in response to natural and anthropogenically induced changes in habitat heterogeneity, food availability, and plant species distribution is essential for developing effective management and conservation programs. Therefore, from July 2013 to June 2014, we examined the effects of landscape configuration on the ranging behavior (daily path length, DPL) of the Endangered Yunnan snub-nosed monkey (Rhinopithecus bieti) in the Baimaxueshan National Nature Reserve (27°34′N, 99°17′E) in Gehuaqing, China. Given the extreme difficulties in following the study group across high altitude mountainous terrain across an elevation of 2,500–4,000 m, we were only able to collect DPL using 3-4 GPS points per day on 21 individual days. We found that R. bieti traveled the shortest DPL in winter (1,141.31 m), followed by spring (2,034.06 m) and autumn (2,131.19 m). The cost distance, a statistical tool designed to estimate the difficulty of a species moving across its distributional range, was lowest in autumn (205.47), followed by spring (225.93) and winter (432.59) (one-way ANOVA: F = 3.852, P = 0.026, df = 2). The habitat fragmentation index (HFI), which measures the density of forest patches, indicated areas visited in the winter were more fragmented (HFI = 2.16) compared to spring (HFI = 1.83) or autumn (HFI = 1.3). Although our results should be considered preliminary, they suggest that both the availability of suitable travel routes and habitat fragmentation, driven by high-intensity human disturbance, constrain the movement of R. bieti. We found that undisturbed areas of the bands’ range contained a high density of lichens, which represent a nutritious and abundant and year-round food source for Yunnan snub-nosed monkeys. In order to protect this Endangered species, we recommend that researchers construct detailed maps of landscape heterogeneity, particularly habitat connectivity, forest fragmentation, and seasonal variation in the location of major food patches in order to better understand and mitigate the effects of seasonal habitat change on patterns of R. bieti habitat utilization and population viability.

BackgroundChina is running a national level antivirus campaign against the novel coronavirus (2019-nCoV). Strict control measures are being enforced in either the populated areas and remote regions. While the virus is closed to be under control, tremendous economic loss has been caused.Methods and findingsWe assessed the pandemic risk of 2019-nCoV for all cities/regions in China using the random forest algorithm, taking into account the effect of five factors: the accumulative and increased numbers of confirmed cases, total population, population density, and GDP. We defined four levels of the risk, corresponding to the four response levels to public health emergencies in China. The classification system has good consistency among cities in China, as the error rate of the confusion matrix is 1.58%.ConclusionsThe pandemic risk of 2019-nCoV is dramatically different among the 442 cities/regions. We recommend to adopt proportionate control policy according to the risk level to reduce unnecessary economic loss.

BackgroundAfter the outbreak of novel coronavirus (2019-nCoV) starting in late 2019, a number of researchers have reported the predicted the virus transmission dynamics. However, under the strict control policy the novel coronavirus does not spread naturally outside Hubei Province, and none of the prediction closes to the real situation.Methods and findingsWe used the traditional SEIR model, fully estimated the effect of control measures, to predict the virus transmission in Wuhan, the capital city of Hubei Province, and Beijing. We forecast that the outbreak of 2019-nCoV would reach its peak around March 6±10 in Wuhan and March 20±16 in Beijing, respectively. The infectious population in Beijing would be much less (only 0.3%) than those in Wuhan at the peak of this transmission wave. The number of confirmed cases in cities inside Hubei Province grow exponentially, whereas those in cities outside the province increase linearly.ConclusionsThe unprecedented province lockdown substantially suspends the national and global outbreak of 2019-nCoV.

The novel coronavirus (2019-nCoV) appeared in Wuhan in late 2019 have infected 34,598 people, and killed 723 among them until 8th February 2020. The new virus has spread to at least 316 cities (until 1st February 2020) in China. We used the traffic flow data from Baidu Map, and number of air passengers who left Wuhan from 1st January to 26th January, to quantify the potential infectious people. We developed multiple linear models with local population and air passengers as predicted variables to explain the variance of confirmed cases in every city across China. We found the contribution of air passengers from Wuhan was decreasing gradually, but the effect of local population was increasing, indicating the trend of local transmission. However, the increase of local transmission is slow during the early stage of novel coronavirus, due to the super strict control measures carried out by government agents and communities.

AIM: In this article, we analysed two millennia of historical records and environmental information to reconstruct the past distribution and examine the current distribution of snub‐nosed monkeys (Rhinopithecus) in China. LOCATION: China. METHODS: We applied trend surface analysis (TSA) to document patterns of range shifting in snub‐nosed monkeys over time. Random forest was used to study the association between explanatory variables and changes in the distribution of snub‐nosed monkeys over the past 2000 years. RESULTS: Our results showed that both the longitude and latitude of snub‐nosed monkeys contracted from 0 to 2000 AD. We found that the integrated effects of human population size and changes in temperature in the Northern Hemisphere resulted in a westward and northward contraction of the snub‐nosed monkey distributional range. However, the impact of fluctuating temperature was greatest during periods of low human population density (0–1200 AD), whereas from 1200 to 2000 AD, marked increases in human population size in China leading to extensive deforestation, agricultural expansion, hunting, logging and land terracing have had the greatest negative effects. Further analyses highlighted the fact that the rapid expansion of human population density in regions occupied by snub‐nosed monkeys between 1700 and 2000 has resulted in the recent extirpation of this primate radiation in eastern, south‐eastern and central China. MAIN CONCLUSIONS: We examined the interactive effects of human population growth, deforestation, agricultural expansion and climate variation on the past and current distribution of snub‐nosed monkeys. Our data provide clear evidence that climate change, human population increase and human activities have differentially affected the viability and distribution of snub‐nosed monkey populations over time. In particular, the marked expansion of the human population in China over the past 300 years has resulted in the extinction of Rhinopithecus populations across much of its range.

In this study, we integrate data from field investigations, spatial analysis, genetic analysis, and Generalized Linear Models (GLMs) to evaluate the effects of habitat fragmentation on the population dynamics, genetic diversity, and range shifts in the endangered Yunnan snub-nosed monkey (Rhinopithecus bieti). The results indicate that from 1994 to 2016, R. bieti population size increased from less than 2,000 to approximately 3,000 individuals. A primary factor promoting population recovery was the establishment of protected nature reserves. We also found that subpopulation growth rates were uneven, with the groups in some areas, and the formation of new groups. Both the fragmentation index, defined as the ratio of the number of forest patches to the total area of forest patches (e.g., increased fragmentation), and increasing human population size had a negative effect on population growth in R. bieti. We recommend that government conservation plans prioritize the protection of particular R. bieti populations, such as the Baimei and Jisichang populations, which have uncommon haplotypes. In addition, effective conservation strategies need to include an expansion of migration corridors to enable individuals from larger populations such as Guyoulong (Guilong) to serve as a source population to increase the genetic diversity of smaller R. bieti subpopulations. We argue that policies designed to protect endangered primates should not focus solely on total population size but also need to determine the amount of genetic diversity present across different subpopulations and use this information as a measure of the effectiveness of current conservation policies and the basis for new conservation policies.