In this study, the applicability of the statistical downscaling model (SDSM) in downscaling precipitation in the Yangtze River basin, China was investigated. The investigation includes the calibration of the SDSM model by using large-scale atmospheric variables encompassing NCEP/NCAR reanalysis data, the validation of the model using independent period of the NCEP/NCAR reanalysis data and the general circulation model (GCM) outputs of scenarios A2 and B2 of the HadCM3 model, and the prediction of the future regional precipitation scenarios. Selected as climate variables for downscaling were measured daily precipitation data from 136 weather stations in the Yangtze River basin. The results showed that: (1) there existed good relationship between the observed and simulated precipitation during the calibration period of 1961-1990 as well as the validation period of 1991-2000. And the results of simulated monthly and seasonal precipitation were better than that of daily. The average R 2 values between the simulated and observed monthly and seasonal precipitation for the validation period were 0.78 and 0.91 respectively for the whole basin, which showed that the SDSM had a good applicability on simulating precipitation in the Yangtze River basin. (2) Under both scenarios A2 and B2, during the prediction period of 2010-2099, the change of annual mean precipitation in the Yangtze River basin would present a trend of deficit precipitation in 2020s; insignificant changes in the 2050s; and a surplus of precipitation in the 2080s as compared to the mean values of the base period. The annual mean precipitation would increase by about 15.29% under scenario A2 and increase by about 5.33% under scenario B2 in the 2080s. The winter and autumn might be the more distinct seasons with more predicted changes of precipitation than in other seasons. And (3) there would be distinctive spatial distribution differences for the change of annual mean precipitation in the river basin, but the most of Yangtze River basin would be dominated by the increasing trend.

A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network ABSTRACT: For the purpose of comparing susceptibility mapping methods in Mizunami City, Japan, the landslide inventory was partitioned into three groups as various training and test datasets to identify the most appropriate method for creating a landslide susceptibility map. A total of fifteen landslide susceptibility maps were produced using frequency ratio, logistic regression, decision tree, weights of evidence and artificial neural network models, and the results were assessed using existing test landside points and areas under the relative operative characteristic curve (AUC). The validation results indicated that the logistic regression model could provide the highest AUC value (0.865), and a relatively high percentage of landslide points fell in the high and very high landslide susceptibility classes in this study. Furthermore, the paper also suggested that the model performances would be increased if appropriate landslide points were used for the calculation.

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BackgroundDendrobium huoshanense C.Z. Tang et S.J. Cheng is a traditional Chinese herbal medicine with high medicinal value in China. Polysaccharides and alkaloids are its main active ingredients. To understand the difference of main active ingredients in different tissues, we determined the contents of polysaccharides and alkaloids in the roots, stems and leaves of D. huoshanense. In order to explore the reasons for the differences of active ingredients at the level of transcription, we selected roots, stems and leaves of D. huoshanenese for transcriptome sequencing and pathway mining.ResultsThe contents of polysaccharides and alkaloids of D. huoshanense were determined and it was found that there were significant differences in different tissues. A total of 716,634,006 clean reads were obtained and 478,361 unigenes were assembled by the Illumina platform sequencing. We identified 1407 carbohydrate-active related unigenes against CAZy database including 447 glycosyltransferase genes (GTs), 818 glycoside hydrolases (GHs), 60 carbohydrate esterases (CEs), 62 carbohydrate-binding modules (CBMs), and 20 polysaccharide lyases (PLs). In the glycosyltransferases (GTs) family, 315 differential expression genes (DEGs) were identified. In total, 124 and 58 DEGs were associated with the biosynthesis of alkaloids in Dh_L vs. Dh_S and Dh_R vs. Dh_L, respectively. A total of 62 DEGs associated with the terpenoid pathway were identified between Dh_R and Dh_S. Five key enzyme genes involved in the terpenoids pathway were identified, and their expression patterns in different tissues was validated using quantitative real-time PCR.ConclusionsIn summary, our study presents a transcriptome profile of D. huoshanense. These data contribute to our deeper relevant researches on active ingredients and provide useful insights into the molecular mechanisms regulating polysaccharides and alkaloids in Dendrobium.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-5305-6) contains supplementary material, which is available to authorized users.

Pine wilt disease is a devastating forest disease caused by the pinewood nematode Bursaphelenchus xylophilus, which has been listed as the object of quarantine in China. Climate change influences species and may exacerbate the risk of forest diseases, such as the pine wilt disease. The maximum entropy (MaxEnt) model was used in this study to identify the current and potential distribution and habitat suitability of three pine species and B. xylophilus in China. Further, the potential distribution was modeled using the current (1970–2000) and the projected (2050 and 2070) climate data based on two representative concentration pathways (RCP 2.6 and RCP 8.5), and fairly robust prediction results were obtained. Our model identified that the area south of the Yangtze River in China was the most severely affected place by pine wilt disease, and the eastern foothills of the Tibetan Plateau acted as a geographical barrier to pest distribution. Bioclimatic variables related to temperature influenced pine trees’ distribution, while those related to precipitation affected B. xylophilus’s distribution. In the future, the suitable area of B. xylophilus will continue to increase; the shifts in the center of gravity of the suitable habitats of the three pine species and B. xylophilus will be different under climate change. The area ideal for pine trees will migrate slightly northward under RCP 8.5. The pine species will continue to face B. xylophilus threat in 2050 and 2070 under the two distinct climate change scenarios. Therefore, we should plan appropriate measures to prevent its expansion. Predicting the distribution of pine species and the impact of climate change on forest diseases is critical for controlling the pests according to local conditions. Thus, the MaxEnt model proposed in this study can be potentially used to forecast the species distribution and disease risks and provide guidance for the timely prevention and management of B. xylophilus.

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