摘要
传统的两栖爬行动物多样性调查方法在进行野外实验时,常遇到抽样限制的问题,一些稀有物种可能无法在个体样本中被发现,由于存在相对较大的物种缺失,导致不同的研究结果差距较大,较难反映真实的物种多样性。因此,基于有限的调查和监测数据尽可能准确地估计生物多样性极其重要。本文于2017—2020年的每年秋季,采用视觉遇见法调查了4 km样线,共记录27种两栖爬行动物,采用贝叶斯权重估计方法估计了浙江省古田山国家级自然保护区两栖爬行动物多样性。保护区内分布有38种两栖爬行动物。已有的实地调查显示,保护区内有36种两栖爬行动物,由此印证了贝叶斯权重估计方法估计结果的准确性。
The traditional methods for survey on herpetological diversity is usually encounter the limiting-sampling problem when conducting in field experiments,some rare species may not be discovered in a sample of individuals because of the existence of many rare species,so the true number of species is often unknown when a relatively large fraction of the species is missing.There is a big gap between different number species of amphibians and reptiles classified in the Gutianshan National Nature Reserve(Zhejiang).As a consequence,it is important to use limited data derived from restricted sampling attempts to predict and explain biodiversity as accurately as possible.In the autumn of 2017-2020,a 4 km long transect line in the reserve was investigated by using visual encounter surveys,and 27 species of amphibian and reptile along the transect line were recorded.We used the Bayesian-weighted approach to estimate the amphibians and reptiles diversity in the reserve.The prediction result showed that there are 38 species of amphibians and reptiles.Finally,we applied 3 real data set of species discovered in the reserve,and 36 species were given,the results showed that the Bayesian-weight estimator performed was quite accurate.
作者
李成
章文艳
高军
杨韬
熊姗
赵春霖
刘萍
王燕
陈有华
戴蓉
LI Cheng;ZHANG Wenyan;GAO Jun;YANG Tao;XIONG Shan;ZHAO Chunlin;LIU Ping;WANG Yan;CHEN Youhua;DAI Rong(Chengdu Institute of Biology,Chinese Academy of Sciences,Chengdu 610041,China;University of Chinese Academy of Sciences,Beijing 100049,China;Nanjing Institute of Environmental Sciences,Ministry of Ecology and Environment of the People s Republic of China,Nanjing 210042,China)
出处
《四川动物》
北大核心
2022年第1期92-98,共7页
Sichuan Journal of Zoology
基金
中国生物多样性监测与研究网络项目(Sino BON)
生态环境部生物多样性调查、观测和评估项目(2019—2023年)
江苏省自然科学基金——青年基金项目(BK20160103)。
