Identification of collembolan species is generally based on specific morphological characters, such as chaetotaxy and pigmentation pattern. However, some specimens do not match to described characters because these refer to adult specimens, often of one specific sex, or the characters are highly variable in adults (e.g. pigmentation, setae or furcal teeth). Isozymes have frequently assisted species discrimination, and also these may vary with developmental stage or environmental conditions. For identification of single species of the Isotoma viridis group, we present both direct sequencing of the cytochrome oxidase subunit II (COII) gene and a simple DNA-based molecular method.
Five PCR primers amplifying the COII region (717 bp) of the mitochondrial DNA were used. The sequences clearly separated the species I. viridis, I. riparia and I. anglicana, irrespective of colour varieties within the first species. DNA amplification products of different species can also be distinguished by digestion with restriction endonucleases, followed by gel electrophoresis for separation of fragments. This restriction fragment length polymorphism (RFLP), obtained after digestion with the endonucleases TaqI, VspI, MvaI and Bsp143I, revealed specific fragments that separated the three species from each other. Since restriction enzymes are sensitive to single base mutations, we suggest to use a combination of enzymes with at least two species-specific restriction sites when using the RFLP technique. For the I. viridis complex, VspI and Bsp143I appear to be an appropriate combination. 相似文献
High quality, agricultural nutrient distribution maps are necessary for precision management, but depend on initial soil sample analyses and interpolation techniques. To examine the methodologies for and explore the capability of interpolating soil properties based on neural network ensemble residual kriging, a silage field at Hayes, Northern Ireland, UK, was selected for this study with all samples being split into independent training and validation data sets. The training data set, comprised of five soil properties: soil pH, soil available P, soil available K, soil available Mg and soil available S,was modeled for spatial variability using 1) neural network ensemble residual kriging, 2) neural network ensemble and 3) kriging with their accuracies being estimated by means of the validation data sets. Ordinary kriging of the residuals provided accurate local estimates, while final estimates were produced as a sum of the artificial neural network (ANN) ensemble estimates and the ordinary kriging estimates of the residuals. Compared to kriging and neural network ensemble,the neural network ensemble residual kriging achieved better or similar accuracy for predicting and estimating contour maps. Thus, the results demonstrated that ANN ensemble residual kriging was an efficient alternative to the conventional geo-statistical models that were usually used for interpolation of a data set in the soil science area. 相似文献
以皖北平原典型农业生产大县亳州市蒙城县为代表,运用统计学、地统计学方法和GIS技术研究了其农田耕作层(0~20 cm)土壤有机碳(SOC)含量的空间分布及其影响因子。结果表明:研究区SOC含量为10.41±2.52 g kg-1,近30年来提高了55.61%,SOC变异系数为24%,属于中等变异程度。SOC含量在空间分布上表现为东北部、中部和西南部含量高,由西北向东南先逐渐增加后逐渐降低,变异程度较高。整个县域范围内SOC空间变异的主要影响因素为土壤机械组成(粉粒和砂粒含量),其次为秸秆还田。 相似文献