Decline in medicinal and forage species with warming is mediated by plant traits on the Tibetan Plateau

Experimental studies of how global changes and human activities affect plant diversity often focus on broad measures of diversity and discuss the implications of these changes for ecosystem function. We examined how experimental warming and grazing affected species within plant groups of direct importance to Tibetan pastoralists: medicinal plants used by humans and palatable plants consumed by livestock. Warming resulted in species losses from both the medicinal and palatable plant groups; however, differential relative vulnerability to warming occurred. With respect to the percent of warming-induced species losses, the overall plant community lost 27%, medicinal plants lost 21%, and non-medicinal plants lost 40% of species. Losses of palatable and non-palatable species were similar to losses in the overall plant community. The deep-rootedness of medicinal plants resulted in lowered sensitivity to warming, whereas the shallow-rootedness of non-medicinal plants resulted in greater sensitivity to warming; the variable rooting depth of palatable and non-palatable plants resulted in an intermediate response to warming. Predicting the vulnerability of plant groups to human activities can be enhanced by knowledge of plant traits, their response to specific drivers, and their distribution within plant groups. Knowledge of the mechanisms through which a driver operates, and the evolutionary interaction of plants with that driver, will aid predictions. Future steps to protect ecosystem services furnished by medicinal and palatable plants will be required under the novel stress of a warmer climate. Grazing may be an important tool in maintaining some of these services under future warming.

人口空间数据误差分析研究

Spatial population data, obtained through the pixeling method, makes many related researches more convenient. However, the limited methods of precision analysis prevent the spread of spatial distribution methods and cumber the application of the spatial population data. This paper systematically analyzes the different aspects of the spatial population data precision, and re-calculates them with the reformed method, which makes breakthrough for the spread of the pixeling method and provides support and reference for the application of spatial population data. The paper consists of the following parts: (2) characters of the error; (2) origins of the error; (3) advancement on the calculating methods of the spatial population data. In the first place, based on the analysis of the error trait, two aspects of the spatial population data precision are characterized and analyzed: numerical character and spatial distributing character. The later one, placed greater emphasis on in this paper, is depicted in two spatial scales: county and town. It is always essential and meaningful to the research in this paper that spatial distribution is as important as numerical value in analyzing error of the spatial distributed data. The result illustrates that the spatial population data error appears spatially in group, although it is random in the aspect of data statistics, all of that shows there lies spatial systematic error. Secondly, this paper comes to conclude and validate the lineal correlation between the residential land area (from 1:50000 map and taken as real area) and population. Meanwhile, it makes particular analysis on the relationship between the residential land area, which is obtained from the land use map and the population in three different spatial scales: village, town and county, and makes quantitative description of the residential density variation in different topological environment. After that, it analyzes the residential distributing traits and precision. With the consideration of the above researches, it reaches the conclusion that the error of the spatial distributed population is caused by a series of factors, such as the compactness of the residents, loss of the residential land, the population density of the city. Eventually, the paper ameliorates the method of pixeling the population data with the help of the analysis on error characters and causes. It tests 2-class regionalization based on the 1-class regionalization of China, and resorts the residential data from the land use map. In aid of GIS and the comprehensive analysis of various data source, it constructs models in each 2-class district to calculate spatial population data. After all, LinYi Region is selected as the study area. In this area, spatial distributing population is calculated and the precision is analyzed. All it illustrates is that new spatial distributing population has been improved much. The research is fundamental work. It adopts large amounts of data in different types and contains many figures to make convincing and detailed conclusions.

聋童与正常儿童中文阅读信息加工过程的比较研究

Based upon the experiments showing three traits of Chinese characters as stimulus, the comparison has been made between the hearing-impaired and normal children in RT functions for the judgment involved in the verbal recording. The results show that the function of semantic code is more strongly correlated with recognition of Chinese character than the phonetic code and graphic code for both the hearing-impaired and normal children. For hearing-impaired children, a direct access that a semantic code is produced directly from the graphic code is observed. However, for normal children the strategly of grapheme-phoneme conversion rules to interpret the meaning of words is used. It's shown that no significant differences of mental processing has been found between the hearing-impaired and normal children. Perhaps both the direct access and the grapheme-phoneme conversion form a comprehensive information procession activating each other. In reading Chinese character, the hearing-impaired and normal children is significantly effected by stroke under and character frequency. It is found that the normal children have more depth processing in reading Chinese sentences than the hearing-impaired children.