高山姬鼠种群数量动态及预测预报模型

为了摸清高山姬鼠种群数量变节变动规律,探讨其种群数量预测方法,采用夹夜法调查逐月捕获率,用捕获率为预测指标,建立种群数量预测预报模型。对1996-2008年贵州省大方县高山姬鼠种群数量动态及种群数量进行分析预测,结果表明:高山姬鼠主要分布于稻田、旱地耕作区,是大方县农田害鼠优势种,占总鼠数的62.32%。10a平均捕获率为(2.58±1.27)%,全年种群数量变动曲线呈单峰型,各年度种群数量的变化曲线基本相似,一年内种群数量在6月份出现1个数量高峰,平均捕获率达(4.63±3.03)%。不同年度、不同月份、不同季节之间种群数量存在显著差异。根据历年高山姬鼠种群数量变动幅度及发生危害情况,结合当地鼠害防治指标,制定了高山姬鼠种群数量分级标准。分析1996-2008年高山姬鼠数量高峰期前各月捕获率、种群繁殖参数(性比、怀孕率、胎仔数、睾丸下降率、繁殖指数)与数量高峰期6月种群密度的关系后发现,4月份种群数量基数与6月份种群密度之间相关极显著,运用回归分析方法,建立了应用4月份种群数量基数(X)预测数量高峰期6月份种群密度(Y)的短期预测预报模型:Y=1.7558X+0.1442,可提前2个月预测当年数量峰种群密度和发生程度,经回测验证,数值和数量级预测值与实测值基本相符,数值预测和数量级预测平均吻合率为92.84%、100.00%,结果比较准确,故该预测预报模型具有一定的实用性和可行性。

The seasonal population dynamics and prediction models of Apodemus chevrieri

In The population dynamics and abundance of Apodemus chevrieri, a primary pest rodent, were studied in Dafang County, Guizhou, from 1996-2008 using toe-clipping, surveys, and modelling from monthly capture rates. Apodemus chevrieri represents 62% of all farmland rats and is stable among years, ranging from 49%-79% of captured rodents. Over the last ten years, the average annual capture rate was 2.58% 1.27%. From 1996 to 1999, the population density was high and the annual average capture rate exceeded 3%. However, population density from 2001 to 2008 was low, and the annual average capture rate was less than 2%. Populations of Apodemus chevrieri showed significant variation with year, month, and season. The highest annual average capture rate of 4.99% was five times higher than the lowest annual average rate. Monthly average catch rates varied by a factor of 39, and average catch rates in the summer were 2.3 times higher than those in winter. Population changes throughout the year typically showed a single peak in June, and were essentially similar among different years. A classification standard was formulated for integration with local rodent control targets, taking into account population fluctuations and damage caused in previous years. Correlations were obtained among capture rate, reproductive parameters, sex ratio, pregnancy rate, litter size, testis growth, monthly reproductive index, and population density in the peak month of June. Using regression analysis, population levels in April can be used to forecast accurately the June population density and maximum numbers. The average match rates for value prediction and magnitude prediction were 92.84% and 100.00%, respectively. This high accuracy indicates practical and feasible forecasting for pest rodent populations and its use in control.