影响黄龙藻类群落结构的环境因子分析

为了探讨引起黄龙钙华沉积区附着藻类大量滋生的原因、持续发展的趋势以及对钙华景观产生的影响,于2015年春、夏、秋季采集景区内12个典型样地的藻样和水样,在进行群落结构分析和环境因子分析的基础上,采用CCA（典型对应分析）以探明影响群落结构的主要环境因子.结果表明:① 试验共检出藻类88种,隶属于5门9纲18目20科37属,优势种以贫营养土著型为主;② 随季节更替,物种丰度、生物量和Margalef指数增加,Pielou指数下降,Shannon-Wiener指数和Simpson指数变化稳定;③ 基于CCA结果发现,pH（F=1.6,P=0.02）和海拔（F=1.5,P=0.04）是影响群落结构的主要环境因子.研究显示,当前环境下,自然因子较人为因子对群落结构的影响更为显著,后期在制订水环境评估及钙华资源保护方案时,应当充分考虑特定环境因子的影响.      

黄龙钙华水体藻多样性及分布规律研究

对黄龙钙华水体藻种类、分布规律及多样性进行了研究,共鉴定出23属53种淡水藻类,分属5门。黄龙钙华水体藻类以硅藻占绝对优势,绿藻、黄藻门、蓝藻门次之,裸藻门、金藻门最少。从季节交替变化来看,春季小球藻最多,夏秋季节硅藻最多;从分布规律来看,夏季以海拔最高的泉眼处藻类最多,沿海拔降低藻类数量递减;秋季以钙华滩流群处藻类最盛。多样性分析结果表明,黄龙水体钙华藻类的物种丰富度指数dmar在0.083⁰.667之间,多样性指数H在0.1910.667之间,多样性指数H在0.191¹.99之间,均匀度e在0.2761.99之间,均匀度e在0.276¹.000之间。结果表明黄龙钙华水体藻类受到季节、温度、水量等环境因数和海拔、钙华滩、彩池等地理因素影响表现出现种群的周期演替。     

Spatial evolution and specialization division of crop planting structure in counties of Tibet,China北大核心CSCD

从农业区域系统的角度分析农作物的空间集聚和专业化,可为农业产业结构调整及优化提供决策依据.以西藏粮食作物、油料作物、蔬菜和饲草4类作物为研究对象,基于1995-2020年西藏农业统计年鉴农作物播种面积数据分析西藏农作物种植面积时序变化,基于1995-2018年统计年鉴农作物播种面积数据和74个县域空间单元,综合运用重心模型、基尼系数、区位熵及空间自相关模型,通过ArcGIS软件分析西藏作物种植空间变化及专业化格局.结果显示:(1)西藏粮食作物种植面积占比历年均占绝对优势,但呈逐年下降趋势,油料作物种植面积整体呈波动性小幅上升,蔬菜和青饲料种植面积逐年明显增长.粮食作物、经济作物(含油料作物和蔬菜)、饲料作物比例从1995年的86:12:2调整到2020年的68:17:14.(2)1995-2015年间西藏粮食和油料生产重心较为稳定,未出现较大范围的地理迁移,其他农作物重心迁移距离较大,从东南向西北迁移416.7 km.(3)西藏农作物均呈现一定程度的空间集聚和区域专业化生产格局,但在研究期内生产集聚及专业化水平呈下降趋势.(4)将西藏粮食、油料和其他农作物划分为绝对优势区、比较优势区、优势衰退区、潜力优势区、不具优势区、优势退出区、可种植区和无种植区8种类型.本研究表明西藏农作物种植结构调整明显,基于生产格局及演变趋势划定了专业化分区,可引导农作物生产布局优化,对有效保障西藏地区粮食安全具有重要意义.(图6表5参26)     

Spatiotemporal patterns and other impacting factors on wheat production of Tibet,China北大核心CSCD

Wheat is the second-largest food crop in Tibet, China. Along with economic development, the pattern of wheat production in Tibet has changed dramatically, potentially affecting the balance of grain supply and demand, as well as food security. A clear understanding of the spatiotemporal patterns and other factors affecting wheat production can provide a necessary basis for the scientific management and efficient production of wheat. Based on the wheat yield from 1990 to 2020 in 74 county-level units of Tibet, this study analyzed the spatial and temporal change patterns of Tibetan wheat production and its impacting factors using the Gini coefficient, center of gravity move method, advantage index, exploratory spatial data analysis (ESDA), and geographically weighted regression model (GWR). Results showed that: (1) the total wheat production in Tibet rose from 1990 to 1999, fluctuated, and then decreased from 1999 to 2020. The production areas are mainly concentrated in the southern Tibetan Valley and counties in the Three Rivers region of eastern Tibet. (2) The spatial distribution and agglomeration pattern of wheat in Tibet is relatively stable, and the production center shows a slight fluctuation trend from west to east during the 1990-2020 time period. (3) Based on the comprehensive advantage index (CAI), Tibet’s wheat production in advantaged areas, slightly advantaged areas, slightly disadvantaged areas, and disadvantaged areas accounted for 13.5%, 8.1%, 9.5%, and 40.5% of the total production, respectively; indicating that the regional distribution of CAI exhibits spatial agglomeration rather than sporadic distribution. The patterns are HH (high-high), LH (low-high), and random; 29 hot spots are mainly distributed in Lhasa, Shannan, and Nyingchi, while the cold spots are mainly distributed in Xigazê and Ngari; and (4) the impacting factors on Tibet’s wheat production mainly include effective irrigation areas and agricultural mechanization level as positive factors, and regional gross agricultural product as the main negative factor. In addition, rainfall, other grain output, livestock inventory at the end of the year, and the number of non-primary industries also have an impact on wheat production. Wheat planting area in Tibet has been declining in recent years, thus exhibiting a significant pattern of spatial agglomeration. The three dominant factors affecting the emerging spatiotemporal pattern of wheat production in Tibet are the irrigation condition, mechanization, and local economic development levels. Based on the above conclusions, the study suggests building a high-yield wheat area consistent with local conditions by strictly protecting arable land, improving irrigation conditions, and increasing machinery investment. Depending on the counties of high-yield areas, Tibetan farmers and herders should cultivate the bases of wheat production and build a wheat base for local wheat consumption. © 2022 Science Press. All rights reserved.