Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Host–parasitoid population density prediction using artificial neural networks: diamondback moth and its natural enemies

Host–parasitoid population density prediction using artificial neural networks: diamondback moth... 1 An integrated pest management (IPM) system incorporating the introduction and field release of Diadegma semiclausum (Hellén), a parasitoid of diamondback moth (DBM) Plutella xylostella (L.), comprising the worst insect pest of the cabbage family, has been developed in Kenya to replace the pesticides‐only approach. 2 Mathematical modelling using differential equations has been used in theoretical studies of host–parasitoid systems. Although, this method helps in gaining an understanding of the system's dynamics, it is generally less accurate when used for prediction. The artificial neural network (ANN) approach was therefore chosen to aid prediction. 3 The ANN methodology was applied to predict the population density of the DBM and D. semiclausum, its larval parasitoid. Two data sets, each from different release areas in the Kenya highlands, and both collected during a 3‐year period after the release of the parasitoid, were used in the present study. Two ANN models were developed using these data. 4 The ANN approach gave satisfactory results for DBM and for D. semiclausum. Sensitivity analysis suggested that pest populations may be naturally controlled by rainfall. 5 The ANN provides a powerful tool for predicting host–parasitoid population densities and made few assumptions on the field data. The approach allowed the use of data collected at any appropriate scale of the system, bypassing the assumptions and uncertainties that could have occurred when parameters are imported from other systems. The methodology can be explored with respect to the development of tools for monitoring and forecasting the population densities of a pest and its natural enemies. In addition, the model can be used to evaluate the relative effectiveness of the natural enemies and to investigate augmentative biological control strategies. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Agricultural and Forest Entomology Wiley

Host–parasitoid population density prediction using artificial neural networks: diamondback moth and its natural enemies

Download PDF
10 pages

Loading next page...
 
/lp/wiley/host-parasitoid-population-density-prediction-using-artificial-neural-CfHbwjyJ0G
Publisher
Wiley
Copyright
© 2010 The Authors. Journal compilation © 2010 The Royal Entomological Society
ISSN
1461-9555
eISSN
1461-9563
DOI
10.1111/j.1461-9563.2009.00466.x
Publisher site
See Article on Publisher Site

Abstract

1 An integrated pest management (IPM) system incorporating the introduction and field release of Diadegma semiclausum (Hellén), a parasitoid of diamondback moth (DBM) Plutella xylostella (L.), comprising the worst insect pest of the cabbage family, has been developed in Kenya to replace the pesticides‐only approach. 2 Mathematical modelling using differential equations has been used in theoretical studies of host–parasitoid systems. Although, this method helps in gaining an understanding of the system's dynamics, it is generally less accurate when used for prediction. The artificial neural network (ANN) approach was therefore chosen to aid prediction. 3 The ANN methodology was applied to predict the population density of the DBM and D. semiclausum, its larval parasitoid. Two data sets, each from different release areas in the Kenya highlands, and both collected during a 3‐year period after the release of the parasitoid, were used in the present study. Two ANN models were developed using these data. 4 The ANN approach gave satisfactory results for DBM and for D. semiclausum. Sensitivity analysis suggested that pest populations may be naturally controlled by rainfall. 5 The ANN provides a powerful tool for predicting host–parasitoid population densities and made few assumptions on the field data. The approach allowed the use of data collected at any appropriate scale of the system, bypassing the assumptions and uncertainties that could have occurred when parameters are imported from other systems. The methodology can be explored with respect to the development of tools for monitoring and forecasting the population densities of a pest and its natural enemies. In addition, the model can be used to evaluate the relative effectiveness of the natural enemies and to investigate augmentative biological control strategies.

Journal

Agricultural and Forest Entomology – Wiley

Published: Aug 1, 2010

References

  • Responses of ovipositing moths to host plant deprivation: life history aspects and implications for intercropping
    Åsman, Åsman; Ekbom, Ekbom
  • Using matrix models to explore the influence of temperature on population growth of arthropod pests
    Bommarco, Bommarco
  • A lumped parameters model for acarine predator‐prey population interactions
    Buffoni, Buffoni; Gilioli, Gilioli
  • Modeling performance and information exchange between fishing vessels with artificial neural networks
    Dreyfus‐Leon, Dreyfus‐Leon; Gaertner, Gaertner
  • Horizontal transmission of the entomapathogenic fungi by the diamondback moth
    Furlong, Furlong; Pell, Pell
  • Evaluation of the impact of natural enemies on Plutella xylostella L. (Lepidoptera: Yponomeutidae) populations on commercial Brassica farms.
    Furlong, Furlong; Zu‐hua, Zu‐hua; Shu‐Sheng, Shu‐Sheng; Zalucki, Zalucki
  • Analysis of biological control of cassava pest in Africa. I. Simulation of carbon nitrogen and water dynamics in cassava.
    Gutierrez, Gutierrez; Wermelinger, Wermelinger; Schulthess, Schulthess; Baumgaertner, Baumgaertner; Herren, Herren; Ellis, Ellis; Yaninek, Yaninek
  • Major mortality factors in the populaton dynamics of the diamondback moth, Plutella maculpennis (Curt.) (Lepidotera:Plutellidae).
    Harcourt, Harcourt
  • The seasonal prevalence of occurrence and the life tables of the Diamondback, Plutella sylostella (L.) (Lepidoptera: Yponomeutidae).
    Iga, Iga
  • Effect of rainfall on a population of the diamondback moth, Plutella xylostella (Lepidoptera: Plutellidae)
    Kobori, Kobori; Amano, Amano
  • Application of neural networks to modeling nonlinear relationships in ecology
    Lek, Lek; Delacoste, Delacoste; Baran, Baran; Dimopoulos, Dimopoulos; Lauga, Lauga; Aulagnier, Aulagnier
  • Classifying soil structure using neural networks
    Levine, Levine; Kimes, Kimes; Sigillito, Sigillito
  • Impact of an exotic parasitoid on Plutella xylostella (Lepidoptera: Plutellidae) population dynamics, damage and indigenous natural enemies in Kenya
    Löhr, Löhr; Gathu, Gathu; Kariuki, Kariuki; Obiero, Obiero; Gichini, Gichini
  • Population dynamics and biological control
    May, May; Hassel, Hassel
  • Artificial neural network approach for modelling and prediction of algal blooms
    Recknagel, Recknagel; French, French; Harkonen, Harkonen; Yabunaka, Yabunaka
  • Developing an empirical model of phytoplankton primary production: a neural network case study
    Scardi, Scardi; Hardi, Hardi
  • Effects of specialist parasitoids on oviposition preference of phytophagous insects: encounter‐dilution effects in a tritrophic interaction
    Shiojiri, Shiojiri; Takabayashi, Takabayashi
  • Population fluctuations of the diamondback moth, Plutella xylostella (L.) on cabbages in Bacillus thuringiensis sprayed and non sprayed lots and factors affecting within‐generation survival of immature.
    Sivapragasam, Sivapragasam; Ito, Ito; Saito, Saito
  • Biology, ecology, and management of the diamondback moth
    Talekar, Talekar; Shelton, Shelton
  • Assessing the impact of biological control of Plutella xylostella through the application of Lotka–Volterra model
    Tonnang, Tonnang; Nedorezov, Nedorezov; Ochanda, Ochanda; Owino, Owino; Löhr, Löhr
  • Artificial neural nets and abundance prediction of aquatic insects in small streams
    Wagner, Wagner; Obach, Obach; Werner, Werner; Schmidt, Schmidt
  • Using artificial neural networks to determine the relative contribution of abiotic factors influencing the establishment of insect pest species
    Watts, Watts; Worner, Worner
  • Modeling alga growth and transport in rivers: a comparison of times series analysis, dynamics, mass balance and neural network techniques
    Whitehead, Whitehead; Howard, Howard; Arulmani, Arulmani
  • Diamondback moth females oviposit more on plants infested by non‐parasitised than by parasitized conspecifics
    Yasuyuki, Yasuyuki; Masayoshi, Masayoshi; Junji, Junji