Confidence interval of intrinsic optimum temperature estimated using thermodynamic SSI model

作     者：Takaya Ikemoto Issei Kurahashi Pei-Jian Shi 

作者机构：Department of Microbiology Teikyo University School of Medicine Department of Planning Information and Management The University of Tokyo Hospital Tokyo Japan State Key Laboratory of Integrated Management of Pest Insects and Rodents Institute of Zoology Chinese Academy of Sciences Beijing China 

出 版 物：《Insect Science》 (昆虫科学（英文版）)

年 卷 期：2013年第20卷第3期

页      面：420-428页

核心收录：

学科分类：090801[农学-水产养殖] 0908[农学-水产] 08[工学] 082303[工学-交通运输规划与管理] 09[农学] 082302[工学-交通信息工程及控制] 0823[工学-交通运输工程] 

主　　题：approximate bootstrap confidence intervals bias-corrected and accelerated bootstrap percentiles development rate temperature 

摘      要：The intrinsic optimum temperature for the development of ectotherms is one of the most important factors not only for their physiological processes but also for ecolog- ical and evolutional processes. The Sharpe-Schoolfield-Ikemoto （SSI） model succeeded in defining the temperature that can thermodynamically meet the condition that at a par- ticular temperature the probability of an active enzyme reaching its maximum activity is realized. Previously, an algorithm was developed by Ikemoto （Tropical malaria does not mean hot environments. Journal of Medical Entomology, 45, 963-969） to estimate model parameters, but that program was computationally very time consuming. Now, investi- gators can use the SSI model more easily because a full automatic computer program was designed by Shi et al. （A modified program for estimating the parameters of the SSI model. Environmental Entomology, 40, 462-469）. However, the statistical significance of the point estimate of the intrinsic optimum temperature for each ectotherm has not yet been determined. Here, we provided a new method for calculating the confidence interval of the estimated intrinsic optimum temperature by modifying the approximate bootstrap confidence intervals method. For this purpose, it was necessary to develop a new program for a faster estimation of the parameters in the SSI model, which we have also done.