摘要
探讨鼠种组成及种群数量变化规律,为农区鼠类预测预报及科学防控提供参考依据.对贵州省余庆县2011-2022 年住宅区、稻田区、旱作区鼠情进行监测分析.结果表明,所捕获的3492 只样本隶属2 目(啮齿目、食虫目)2 科(鼠科、鼩鼱科)10 种,即黑线姬鼠、褐家鼠、黄胸鼠、小家鼠、高山姬鼠、社鼠、针毛鼠、白腹鼠、红耳巢鼠、鼩鼱.住宅区鼠种种类有3 种,以褐家鼠为优势种,占总鼠数的88.57%;稻田区、早作区鼠种种类有10 种,以黑线姬鼠为优势种,平均占总鼠数的83.42%.不同时期鼠种组成明显不同,住宅区褐家鼠、稻田区和早作区黑线姬鼠是当地监测和防治的主要对象.与当地1986-2010 年相比,鼠类种群数量呈明显下降趋势,总平均捕获率为4.79%,其中,住宅区、稻田区、早作区平均捕获率分别为0.24%、6.35%、5.48%.不同年度、月份、季节鼠类种群数量存在明显差异.
Abstract
This paper discussed the variation regularity of the composition and population size of the rodent species,so as to provide a reference for prediction,scientific prevention and control of rodents in agricultural areas.We monitored the rodent populations in residential area,paddy field and dry farming area in Yuqing county from 2011 to 2022.The results showed that 3492 samples were captured,which belonged to 2 orders(Rodentia and Insectivora),2 families(Muridae and Soricidae)and 10 species,i.e,Apodemus agrarius,Rattus norvegicus,Rattus tanezumi,Mus musculus,Apodemus chevrieri,Niviventer lotipes,Niviventer fulvescens,Niviventer coxingi,Micromys erythrotis,Crocidura spp.There were 3 species in residential areas,and R.norvegicus was the dominant species,accounting for 88.57%of the total number of rats.Ten species were found in paddy fields and early cropping areas,and A.agrarius was the dominant species,accounting for 83.42%of the total number of rats on average.The composition of rats was obviously different in different periods.R.norvegicus in residential areas and A.agrarius in paddy fields and early cropping areas were the main targets for local monitoring and control.Compared with the population in this area from 1986 to 2010,the population of rodents showed an obvious decreasing trend with an average capture rate of 4.79%,among which the average capture rates in residential areas,rice fields and early cropping areas were 0.24%,6.35%and 5.48%respectively.There are obvious differences in rodent populations in different years,months and seasons.
基金项目
贵州省高层次创新型人才培养项目(黔科合人才[2015]4019)
遵义市市级人才基地建设项目(第一批)(遵委[2019]69)
NETL
NSTL
万方数据