A Modified Program for Estimating the Parameters of the SSI Model

Shi, Peijian; Ikemoto, Takaya; Egami, Chikahiro; Sun, Yucheng; Ge, Feng

doi:10.1603/en10265

Cited by 54 publications

(60 citation statements)

References 25 publications

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“…If the investigators want to calculate the LDT, they have to use the linear model for its estimation (e.g., Bonato et al 2007;eliopoulos et al 2010). The Sharpe-SchoolfieldIkemoto model assumes only the existence of LDT (Ikemoto 2005(Ikemoto , 2008Shi et al 2011b). The WangLan-Ding model (Wang et al 1982), Lactin (Lactin et al 1995), and performance models (Shi & Ge 2010;Shi et al 2011a) are models whereas the existence of both LDT and UDT is assumed.…”

Section: Resultsmentioning

confidence: 99%

“…It indicates that we need not to know the complex mechanism of the effect of temperature on development rate but we can use a non-parametric model to reflect the relationship between development data and temperature very well. Frankly speaking, although the Sharpe-Schoolfield-Ikemoto model (Sharpe & DeMichele 1977;Schoolfield et al 1981;Ikemoto 2005Ikemoto , 2008Shi et al 2011b) and another model proposed recently by Ratkowsky et al (2005) based on thermodynamics have perfectly combined the temperature-dependent development rates to the probability of an enzyme being in the active state, the mechanisms of temperature on the enzymes that control development and the types of these controlling enzymes are still unknown. Therefore, it is necessary to use a non-parametric fitting method.…”

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confidence: 99%

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Using the loess method to describe the effect of temperature on development rate

Liu¹,

Yan²,

Mingfu³

et al. 2017

Plant Prot. Sci.

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Liu J., Yan Y., Yu M., Parajulee † M.N., Shi P., Liu J., Zhao Z. (2017): Using the LoeSS method to describe the effect of temperature on development rate. Plant Protect. Sci., 53: 226-231.Temperature has a significant influence on development rates of insects and mites. Many parametric models were built to describe the temperature-dependent development rates. However, these models provided different shapes of the curves of development rate versus temperature. For different datasets, investigators have to spend much time on considering which the parametric model is the best for describing the temperature-dependent development rates. In the present study, we encourage investigators to use an important non-parametric model, the LoeSS method, which belongs to local regression methods. The LoeSS method is used to fit some published data on the development rate of aphids to check the goodness-of-fit. We find that the LoeSS method is very flexible for fitting the given datasets. Thus, we consider that the LoeSS method can be used to describe the effect of temperature on the development rate of insects or mites.Keywords: non-parametric model; local regression; weighted least squares; AphididaeIn general, one insect or mite species needs to experience different developmental stages in its lifecycle. Completing each developmental stage requires a period of time, which is referred to as development time (or development duration). The reciprocal characteristic of development time is referred to as development rate. It has been demonstrated that temperature has a strong influence on development rate (Campbell et al. 1974;Taylor 1981). Development rate is regarded as a linear function of temperature over the range of moderate temperatures (Campbell et al. 1974;Zhao et al. 2012). However, this linear relationship between development rate and temperature does not fit if temperatures were not in this range. Several non-linear models have been developed for describing the effect of temperature on development rate (e.g., Logan et al.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Using the loess method to describe the effect of temperature on development rate

Liu¹,

Yan²,

Mingfu³

et al. 2017

Plant Prot. Sci.

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“…On the other hand, the non-linear thermodynamics model describes the developmental rate over a wide range of temperatures and estimates of optimum temperatures for development. The equation of the non-linear thermodynamics (Sharpe-Schoolfield-Ikemoto [SSI]) model can be expressed as follows (Ikemoto 2005(Ikemoto , 2008Shi et al 2011):…”

Section: Life-table Parametersmentioning

confidence: 99%

Temperature-dependent development and reproductive traits of Tetranychus macfarlanei (Acari: Tetranychidae)

et al. 2012

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Development and reproductive traits of Tetranychus macfarlanei Baker & Pritchard (Acari: Tetranychidae) were investigated on kidney bean, Phaseolus vulgaris L., at eleven constant temperatures. Tetranychus macfarlanei was able to develop and complete its life cycle at temperatures ranging from 17.5 to 37.5°C. At 15 and 40°C, a few eggs (2-4%) hatched but further development was arrested. Development from egg to adult was slowest at 17.5°C and fastest at 35°C for both females and males. Using Ikemoto and Takai's linear model, the estimated lower developmental thresholds for egg-to-female adult, egg-to-male adult and egg-to-egg development were 12.9-13.0°C. The thermal constants for the respective stages were 110.85, 115.99 and 125.32 degree-days (DD). The intrinsic optimum temperatures (T (Φ)) calculated by non-linear SSI model were determined as 24.4, 24.4 and 24.2°C for egg-to-female adult, egg-to-male adult and egg-to-egg development, respectively. The net reproductive rate (R (0)) was highest at 25°C (167.4 females per female) and lowest at 17.5°C (42.6 females per female). The intrinsic rate of natural increase, r (m), increased linearly with the rising of temperature from 0.102 at 17.5°C to 0.441 day(-1) at 35°C. These values suggested that T. macfarlanei could be growing quickly in response to increasing temperatures from 17.5 to 35°C and provide a basis for predicting its potential geographical range.

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“…Sharpe-Schoolfield-Ikemoto (SSI) model, a non-linear thermodynamic model improved by Ikemoto (2005Ikemoto ( , 2008 and Shi et al (2011) on the basis of the SS model developed by Sharpe and DeMichele (1977) and Schoolfield et al (1981), was used. The SSI model expression is as follows:…”

Section: Temperature Thresholds and Thermal Requirementsmentioning

confidence: 99%

“…Ikemoto (2005) devised a program for estimating the parameters in the SSI model. 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.…”

Section: Temperature Thresholds and Thermal Requirementsmentioning

confidence: 99%