Confidence interval of intrinsic optimum temperature estimated using thermodynamic SSI model

Abstract

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 ecological and evolutional processes. The Sharpe–Schoolfield–Ikemoto (SSI) model succeeded in defining the temperature that can thermodynamically meet the condition that at a particular 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, investigators 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.

How to Compare the Lower Developmental Thresholds

“…This could be one of the reasons for the incomplete development of the larval stage at 34 and 35° C. The intrinsic optimum temperature along with its confidence interval could be used as an indicator for the geographical distribution and place of origin of related species (Ikemoto 2003). This is also a potential tool for the construction of a phylogenetic tree within a taxon (Ikemoto et al 2012). …”

“…Shi et al (2011) modified this program and developed SSI-P, which runs on R statistical software (www.rproject.org) for faster estimation of the parameters. Ikemoto et al (2012) further improved this program, creating OptimSSI-P by incorporating the optimization algorithm of Nelder and Mead (1965), wherein T Φ was estimated along with its confidence intervals. In this paper, OptimSSI program (version 2.7), which runs on R statistical software, version 2.15.0 (provided by Dr Peijian Shi, Chinese Academy of Sciences) was used to estimate the thermo-dynamic model parameters.…”

Temperature-dependent development of Lista haraldusalis (Walker) (Lepidoptera: Pyralidae) on Platycarya strobilacea

“…With the exceptions of Performance (Shi and Ge, 2010) and SSI (Ikemoto et al, 2013), Logistic, Briere-1, Lactin-1, Lactin-2, and Logan-6 were calculated using the nlinfit function in MATLAB 7.0 (The MathWorks, Inc., MA, USA) (Math, 2005) and nonlinear regressions in SPSS v 17.0 (SPSS Inc., 2008). The parameters of the Performance and SSI models were estimated using the programs developed by Shi et al (2011b) and Ikemoto et al (2013), respectively, which run in the R 2.11.1 statistical software package (R: A Language and Environment for Statistical Computing, Vienna, Austria) (R, 2010). Sigma Plot 10 software (Systat Software, Inc., San Jose, CA, USA) (SigmaPlot, 2006) and R 2.11.1 statistical software (R, 2010) were used to plot the regressions of the linear and nonlinear models.…”

“…Sharpe and DeMichele (1977), Schoolfield et al (1981), Ikemoto (2005), Ikemoto (2008), Ikemoto et al (2013) Appendix A. Nonlinear models that were used to describe the effect of temperature on the developmental rate of L. haraldusalis…”

Development and reproduction of five Tetranychus species (Acari: Tetranychidae): Do they all have the potential to become major pests?

“…Finally, Ikemoto et al (2013) improved their model and developed the program OptimSSI-P, which estimates T U along with its confidence limit.…”