The occurrence periods of Semiaphis heraclei Takahashi,Frankliniella sp.,Haptonchus luteolus and Microsphara linicerae Enchson wint.in Rabenh.causing damage on Flos lonicerae were investigated in F.lonicerae planting ...The occurrence periods of Semiaphis heraclei Takahashi,Frankliniella sp.,Haptonchus luteolus and Microsphara linicerae Enchson wint.in Rabenh.causing damage on Flos lonicerae were investigated in F.lonicerae planting area in XinCheng county of Guangxi Province during 2008-2010,which were coincided with the occurrence periods of related phenology of local Prunus persica Rootstock.With P.persica Rootstock as indicator plant,the occurrence periods of three species of pests and one species of disease were predicted,respectively,and the method was simple and accurate,which could be the foundation for preventing these pests and diseases in the local field.展开更多
Long-term forecasts of pest pressure are central to the effective managementof many agricultural insect pests. In the eastern cropping regions of Australia, seriousinfestations of Helicoverpa punctigera (Wallengren) a...Long-term forecasts of pest pressure are central to the effective managementof many agricultural insect pests. In the eastern cropping regions of Australia, seriousinfestations of Helicoverpa punctigera (Wallengren) and H. armigera (Hiibner)(Lepidoptera:Noctuidae) are experienced annually. Regression analyses of a long series of light-trap catches ofadult moths were used to describe the seasonal dynamics of both species. The size of the springgeneration in eastern cropping zones could be related to rainfall in putative source areas in inlandAustralia. Subsequent generations could be related to the abundance of various crops inagricultural areas, rainfall and the magnitude of the spring population peak. As rainfall figuredprominently as a predictor variable, and can itself be predicted using the Southern OscillationIndex (SOI), trap catches were also related to this variable. The geographic distribution of eachspecies was modelled in relation to climate and CLIMEX was used to predict temporal variation inabundance at given putative source sites in inland Australia using historical meteorological data.These predictions were then correlated with subsequent pest abundance data in a major croppingregion. The regression-based and bio-climatic-based approaches to predicting pest abundance arecompared and their utility in predicting and interpreting pest dynamics are discussed.展开更多
Ecological regime shift is the rapid transition from one stable community structure to another, often ecologically infe- rior, stable community. Such regime shifts are especially common in shallow marine communities, ...Ecological regime shift is the rapid transition from one stable community structure to another, often ecologically infe- rior, stable community. Such regime shifts are especially common in shallow marine communities, such as the transition of kelp forests to algal turfs that harbour far lower biodiversity. Stable regimes in communities are a result of balanced interactions be- tween species, and predicting new regimes therefore requires an evaluation of new species interactions, as well as the resilience of the ＇stable＇ position. While computational optimisation techniques can predict new potential regimes, predicting the most likely community state of the various options produced is currently educated guess work. In this study we integrate a stable regime op- timisation approach with a Bayesian network used to infer prior knowledge of the likely stress of climate change （or, in practice, any other disturbance） on each component species of a representative rocky shore community model. Combining the results, by calculating the product of the match between resilient computational predictions and the posterior probabilities of the Bayesian network, gives a refined set of model predictors, and demonstrates the use of the process in determining community changes, as might occur through processes such as climate change. To inform Bayesian priors, we conduct a review of molecular approaches applied to the analysis of the transcriptome of rocky shore organisms, and show how such an approach could be linked to meas- ureable stress variables in the field. Hence species-specific microarrays could be designed as biomarkers of in situ stress, and used to inform predictive modelling approaches such as those described here.展开更多
基金Supported by Natural Scientific Research Topics of Guangxi Scienceand Technology Department(GKG0992003B-40)Natural Scientific Research Topics of Guangxi Education Department(GJKY200809MS196)~~
文摘The occurrence periods of Semiaphis heraclei Takahashi,Frankliniella sp.,Haptonchus luteolus and Microsphara linicerae Enchson wint.in Rabenh.causing damage on Flos lonicerae were investigated in F.lonicerae planting area in XinCheng county of Guangxi Province during 2008-2010,which were coincided with the occurrence periods of related phenology of local Prunus persica Rootstock.With P.persica Rootstock as indicator plant,the occurrence periods of three species of pests and one species of disease were predicted,respectively,and the method was simple and accurate,which could be the foundation for preventing these pests and diseases in the local field.
文摘Long-term forecasts of pest pressure are central to the effective managementof many agricultural insect pests. In the eastern cropping regions of Australia, seriousinfestations of Helicoverpa punctigera (Wallengren) and H. armigera (Hiibner)(Lepidoptera:Noctuidae) are experienced annually. Regression analyses of a long series of light-trap catches ofadult moths were used to describe the seasonal dynamics of both species. The size of the springgeneration in eastern cropping zones could be related to rainfall in putative source areas in inlandAustralia. Subsequent generations could be related to the abundance of various crops inagricultural areas, rainfall and the magnitude of the spring population peak. As rainfall figuredprominently as a predictor variable, and can itself be predicted using the Southern OscillationIndex (SOI), trap catches were also related to this variable. The geographic distribution of eachspecies was modelled in relation to climate and CLIMEX was used to predict temporal variation inabundance at given putative source sites in inland Australia using historical meteorological data.These predictions were then correlated with subsequent pest abundance data in a major croppingregion. The regression-based and bio-climatic-based approaches to predicting pest abundance arecompared and their utility in predicting and interpreting pest dynamics are discussed.
文摘Ecological regime shift is the rapid transition from one stable community structure to another, often ecologically infe- rior, stable community. Such regime shifts are especially common in shallow marine communities, such as the transition of kelp forests to algal turfs that harbour far lower biodiversity. Stable regimes in communities are a result of balanced interactions be- tween species, and predicting new regimes therefore requires an evaluation of new species interactions, as well as the resilience of the ＇stable＇ position. While computational optimisation techniques can predict new potential regimes, predicting the most likely community state of the various options produced is currently educated guess work. In this study we integrate a stable regime op- timisation approach with a Bayesian network used to infer prior knowledge of the likely stress of climate change （or, in practice, any other disturbance） on each component species of a representative rocky shore community model. Combining the results, by calculating the product of the match between resilient computational predictions and the posterior probabilities of the Bayesian network, gives a refined set of model predictors, and demonstrates the use of the process in determining community changes, as might occur through processes such as climate change. To inform Bayesian priors, we conduct a review of molecular approaches applied to the analysis of the transcriptome of rocky shore organisms, and show how such an approach could be linked to meas- ureable stress variables in the field. Hence species-specific microarrays could be designed as biomarkers of in situ stress, and used to inform predictive modelling approaches such as those described here.
