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作 者:王海银[1] 张志杰[1] 周艺彪[1] 姜庆五[1]
机构地区:[1]复旦大学公共卫生学院流行病学教研室,上海200032
出 处:《复旦学报(医学版)》2009年第2期138-141,148,共5页Fudan University Journal of Medical Sciences
基 金:国家自然科学基金重大项目(30590374);国家高技术研究发展计划(863计划)(2006AA02Z402);上海市重点学科建设项目(B118)
摘 要:目的探索洞庭湖区钉螺分布的动态变化,为钉螺生态学及螺口动力学提供理论依据。方法选择湖南省岳阳市君山区洞庭湖的一块草滩为研究现场,在2007年10月至2008年5月洞庭湖未涨水期间进行随机抽样查螺,实验室内压螺鉴定死活后分框计数。首先计算中位数、四分位间距及聚集度指标等进行统计描述,然后采用最大似然法拟合负二项分布、对数正态分布、指数分布等不同的分布类型以探索不同时间点的钉螺分布状态。结果观察期间各月份钉螺的分布均为正偏态分布,钉螺密度的中位数分别为7、9、1、0、3、4和1(只/0.01m2),四分位间距分别为13、8、3、2、4、6和4;方差均大于均数,聚集性指数随密度同方向变化。水退初期10月份的钉螺数据对几种分布均不能较好拟合,11月份的钉螺分布呈对数正态分布,2008年4月份的钉螺分布同时符合负二项分布和指数分布,其它各月份的钉螺分布均呈负二项分布。结论钉螺分布不是单一的负二项分布,可能存在着与密度有关的一种动态变化,有待于提出新的、更广义的分布模型来更好地反映这种动态变化。Objective To study the distribution of the Oncomelania hupensis and to supply theoretical information for snail ecology and population dynamics. Methods A bottomland close to Junshan Park in Yueyang City, Hunan Province was selected as the study field. The whole study period was from October, 2007 to May, 20(18. Random sampling was designed to determine the sites of investigation where all the snails were collected and the number of live snails was counted by the method of dissection in the laboratory. Statistical indexes of snails such as median, quartile range and aggregation index were first calculated to conduct a preliminary analysis. Then, negative binomial distribution,log-normal distribution, exponential distribution etc were fitted to the snail data by the method of maximum likelihood estimation to explore the snail distribution in different time. Results The snail distribution in the whole study period was all positively skewed distribution. The median of the snail density was 7,9,1,0,3,4 and lper 0. 01m2 ,respectively; the quartile range was 13,8,3,2,4, 6 and 4,respectively. The variance was larger than the mean in each month, and the aggregation index changed with the snail density in the same direction. No distribution was successfully fitted to the snail data from the October; the snail data in the November was approximately log-normal distribution; both the exponential distribution and negative binomial distribution were fitted well to the snail data from April, 2008, while the snail data from other months were all approximately negative binomial distribution. Conclusions The snail distribution is not a unique negative binomial distribution,but is possibly a dynamic distribution related with snail density. A new generalized distribution model is needed to reflect the dynamics of the distribution.
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