摘要
为提供一种更科学、全面的三倍体诱导盐度综合评价方法,本研究采用了两种新的统计学方法对低盐诱导太平洋牡蛎(Crassostrea gigas)幼虫三倍体的效果进行了评估。利用概率加权回归拟合构建了低盐诱导太平洋牡蛎三倍体过程中卵裂率、孵化率与盐度的Probit回归模型,得到了不同诱导盐度条件下卵裂率及孵化率的估计值,其中50%卵裂率及50%孵化率的诱导盐度估计值分别为5.648和8.644,对该模型的可信性进行了验证。应用主成分分析法得到了两个可以代表卵裂率、孵化率和三倍体率的主成分和不同诱导盐度的综合评价指数,其中盐度8得分最高,为0.67,其次为盐度10,得分为0.54。研究结果表明,要达到50%孵化率,诱导盐度需大于8.644;盐度8和10均为合适的诱导盐度,其中盐度8诱导率更高、存活率较低,盐度10存活率更高、诱导率稍低。
To provide a more scientific and comprehensive evaluation system of triploid inducing conditions,weighted probit regression analysis was used in this study to investigate the relationship between cleavage rate,hatching rate and inducing salinity in the hypotonic triploid induction of Pacific oyster Crassostrea gigas.Two models were established,and subsequently verified by pared-samples T test.According to these models,estimated cleavage rate and hatching rate under certain inducing salinity were generated.The salinity at the cleavage rate and hatching rate of 50%was 5.648 and 8.644,respectively.The principal component analysis(PCA)was performed to create a new evaluation system.Salinity of 8 was proved to be the best inducing salinity with the highest comprehensive index of 0.67,followed by 10 with an index of 0.54.Further data on growth,survival,nutrient content and stress resistance were needed to improve this evaluation system and to determine a more suitable inducing condition.In general,inducing salinity needed to be higher than 8.644 to reach the hatching rate of 50%;both 8 and 10 were determined to be the most applicable inducing salinities,of which 8 has better inducing rate while 10 has higher survival rate.
作者
李阳春
王昭萍
刘剑
马培振
LI Yang-Chun;WANG Zhao-Ping;LIU Jian;MA Pei-Zhen(The Key Laboratory of Mariculture(Ocean University of China),Ministry of Education,Qingdao 266003,China;Haiyi Marine Seeds Co.Ltd,Yantai 264000,China)
出处
《中国海洋大学学报（自然科学版）》
CAS
CSCD
北大核心
2020年第4期21-28,共8页
Periodical of Ocean University of China
基金
国家自然科学基金项目(31172403)资助。
关键词
太平洋牡蛎
低盐
三倍体诱导
概率加权回归
主成分分析
Crassostrea gigas
low salinity
triploid induction
weighted probit regression
principal component analysis
