Modeling population dynamics of two cockroach species: Effects of the circadian clock, interspecific competition and pest control

Wu, Hsin-Hue; Lee, How‐Jing; Horng, Shwu‐Bin; Berec, Luděk

doi:10.1016/j.jtbi.2007.08.014

Cited by 3 publications

(3 citation statements)

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“…Dynamic crop models are frequently used in ecology, agronomy and environmental sciences for simulating crop and environmental variables of interest at a discrete time step. These models are useful for the management of endangered species (e.g Santangelo et al ., 2007), for understanding intraspecific and interspecific competition (e.g Yakubu et al ., 2002 ;Wu et al ., 2007), for pest management (e.g Matsuoka and Seno, 2008), for predicting plant growth (e.g Passioura, 1996) or for greenhouse gas management (Gabrielle et al ., 2006b). For instance, CERES-EGC is a discrete-time model that simulates the emission of nitrous oxide (N 2 O), a potent greenhouse gas, into the atmosphere on a daily time step (Gabrielle et al ., 2006a).…”

Section: Introductionmentioning

confidence: 99%

Multivariate global sensitivity analysis for dynamic crop models

Lamboni

Makowski

Lehuger

et al. 2009

Field Crops Research

135

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Dynamic crop models are frequently used in ecology, agronomy and environmental sciences for simulating crop and environmental variables at a discrete time step. They often include a large number of parameters whose values are uncertain, and it is often impossible to estimate all these parameters accurately. A common practice consists in selecting a subset of parameters by global sensitivity analysis, estimating the selected parameters from data, and setting the others to some nominal values. For a discrete-time model, global sensitivity analyses can be applied sequentially at each simulation date. In the case of dynamic crop models, simulations are usually computed at a daily time step and the sequential implementation of global sensitivity analysis at each simulation date can result in several hundreds of sensitivity indices, with one index per parameter per simulation date. It is not easy to identify the most important parameters based on such a large number of values. In this paper, an alternative method called multivariate global sensitivity analysis was investigated. More precisely, the purposes of this paper are (i) to compare the sensitivity indices and associated parameter rankings computed by the sequential and the multivariate global sensitivity analyses, (ii) to assess the value of multivariate sensitivity analysis for selecting the model parameters to estimate from data. Sequential and multivariate sensitivity analyses were compared by using two dynamic models: a model simulating wheat biomass named WWDM and a model simulating N2O gaseous emission in crop fields named CERES-EGC. N2O measurements collected in several experimental plots were used to evaluate how parameter selection based on multivariate sensitivity analysis can improve the CERES-EGC predictions. The results showed that sequential and multivariate sensitivity analyses provide modellers with different types of information for models which exhibit a high variability of sensitivity index values over time. Conversely, when the parameter influence is quite constant over time, the two methods give more similar results. The results also showed that the estimation of the parameters with the highest sensitivity indices led to a strong reduction of the prediction errors of the model CERES-EGC

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Section: Introductionmentioning

confidence: 99%

Multivariate global sensitivity analysis for dynamic crop models

Lamboni

Makowski

Lehuger

et al. 2009

Field Crops Research

135

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“…Modelling of interspecific competition between B. germanica and B. bisignata further suggests that B. germanica can invade and expel an established population of B. bisignata indoors. Moreover, B. germanica can prevent a returning invasion from B. bisignata (Wu et al , ) (Fig. B).…”

Section: Noncircadian Factors That Favour B Germanica To Become Domimentioning

confidence: 98%

“…Interspecific competition between cockroaches, Blattella germanica and Blattella bisignata . A summary of data from (A) Tsai & Lee () and (B) Wu et al () is provided. (A) Both of these two species started with 10 pairs of adults being cultured and monitored within a plastic jar (2 L) for 3 months.…”

Section: Noncircadian Factors That Favour B Germanica To Become Domimentioning

confidence: 99%

Adaptation of the cockroachBlattella germanicato human habitats: circadian and noncircadian factors

2016

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Abstract. To survive, an animal must respond to the environmental stimuli (Zeitgebers) precisely coinciding with a 24-h light/dark cycle. In human habitats, both natural and artificial Zeitgebers intermingle, entraining the circadian clocks of animals. Whether a circadian clock can respond to Zeitgebers effectively is closely related to the level of adaptive competitiveness. In the present review, the German cockroach (Blattella germanica L.) is compared with the double-striped cockroach (Blattella bisignata Brunner von Wattenwyl) and the reasons why B. germanica is a more competitive dweller than its sibling species in human habitats are discussed. For example, sensitive adjustment of circadian clocks allows a male B. germanica to coordinate itself to the changes of daily light/dark cycles more efficiently; by contrast, it takes male B. bisignata more than 3 days to adjust. In a given dark phase, male B. germanica appear more to be resistant than B. bisignata to interference from light pulses. Ovary-dependent masking factors enable female B. germanica to take advantage of the light phase to feed or mate, during which time a female B. bisignata typically remains inert. Furthermore, the gregariousness levels, fertilization and hatching rates, as well as mating behaviours of both cockroach species are compared. All of these comparisons suggest that B. germanica surpasses B. bisignata with respect to adapting to the artificial constraints within human habitats as a result of the adoption of an integrated strategy consisting of circadian and noncircadian approaches.

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