Javascript must be enabled to continue!
A spatial model for the successful biological control of Sitona discoideus by Microctonus aethiopoides
View through CrossRef
Summary
Here we describe a new model for the biological control of the pest weevil Sitona discoideus by the parasitoid Microctonus aethiopoides. Based on an earlier empirical model, the new model was expanded to include explicit dispersal in a coupled map lattice metapopulation. Model results were consistent with observed data from New Zealand, mimicking successful biological control with a reduction in host density from 1984 to 1991 and a sustained suppression of around 75% thereafter.
The metapopulation model was locally stable because of strong host density‐dependence and a non‐random parasitoid attack function. Metapopulation structure had little effect on the local dynamics, except in transient behaviour such as initial rates of parasitoid spread or the response to a local perturbation. The survival rate of dispersing weevils was nevertheless important in determining overall weevil abundance.
Assuming that the observed low survival rate of weevils during dispersal (around 0·3%) was related to the relative scarcity of its host‐plant in the landscape, the model suggested that local weevil density could substantially increase with an increase in area of crop planted. Although the extent of biological control would be sustained in relative terms, increasing the crop abundance could allow a substantial increase in absolute pest density.
With appropriate adjustment, the model also simulated the unsuccessful biological control observed in Australia. Advancing weevil oviposition in autumn (reflecting warmer autumn temperatures in Australia) and reducing parasitism rates among aestivating weevils (reflecting a lack of summer development of parasitoids in Australia, compared with the atypical development of a proportion in New Zealand), led to an inability of the parasitoid to significantly reduce weevil populations in the model.
This host–parasitoid system is unique for the quality and duration of monitoring conducted before, during and after parasitoid introduction. It also represents one of the few biological control case studies to be thoroughly evaluated through use of empirical models such as the spatial and non‐spatial versions presented here.
Title: A spatial model for the successful biological control of Sitona discoideus by Microctonus aethiopoides
Description:
Summary
Here we describe a new model for the biological control of the pest weevil Sitona discoideus by the parasitoid Microctonus aethiopoides.
Based on an earlier empirical model, the new model was expanded to include explicit dispersal in a coupled map lattice metapopulation.
Model results were consistent with observed data from New Zealand, mimicking successful biological control with a reduction in host density from 1984 to 1991 and a sustained suppression of around 75% thereafter.
The metapopulation model was locally stable because of strong host density‐dependence and a non‐random parasitoid attack function.
Metapopulation structure had little effect on the local dynamics, except in transient behaviour such as initial rates of parasitoid spread or the response to a local perturbation.
The survival rate of dispersing weevils was nevertheless important in determining overall weevil abundance.
Assuming that the observed low survival rate of weevils during dispersal (around 0·3%) was related to the relative scarcity of its host‐plant in the landscape, the model suggested that local weevil density could substantially increase with an increase in area of crop planted.
Although the extent of biological control would be sustained in relative terms, increasing the crop abundance could allow a substantial increase in absolute pest density.
With appropriate adjustment, the model also simulated the unsuccessful biological control observed in Australia.
Advancing weevil oviposition in autumn (reflecting warmer autumn temperatures in Australia) and reducing parasitism rates among aestivating weevils (reflecting a lack of summer development of parasitoids in Australia, compared with the atypical development of a proportion in New Zealand), led to an inability of the parasitoid to significantly reduce weevil populations in the model.
This host–parasitoid system is unique for the quality and duration of monitoring conducted before, during and after parasitoid introduction.
It also represents one of the few biological control case studies to be thoroughly evaluated through use of empirical models such as the spatial and non‐spatial versions presented here.
Related Results
Yonca (Medicago sativa L.) Tarımı Yapılan Alanlarda Sitona Germar 1817 (Coleoptera: Curculionidae) Türleri, Dağılımları ve Popülasyon Gelişimleri: Türkiye, Iğdır İli Yonca Alanları
Yonca (Medicago sativa L.) Tarımı Yapılan Alanlarda Sitona Germar 1817 (Coleoptera: Curculionidae) Türleri, Dağılımları ve Popülasyon Gelişimleri: Türkiye, Iğdır İli Yonca Alanları
Bu çalışma, Türkiye’nin Doğu Anadolu Bölgesi’nde yer alan Iğdır ilinde ekimi yapılan yonca bitkisi (Medicago sativa L.) zararlılarından Sitona Germar türlerini belirlemek amacıyla ...
Modeling and control of PEM fuel cells
Modeling and control of PEM fuel cells
In recent years, the PEM fuel cell technology has been incorporated to the R&D plans of many key companies in the automotive, stationary power and portable electronics sectors....
Imputation of Spatially-resolved Transcriptomes by Graph-regularized Tensor Completion
Imputation of Spatially-resolved Transcriptomes by Graph-regularized Tensor Completion
AbstractHigh-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-w...
Establishment and Application of the Multi-Peak Forecasting Model
Establishment and Application of the Multi-Peak Forecasting Model
Abstract
After the development of the oil field, it is an important task to predict the production and the recoverable reserve opportunely by the production data....
Flexible Bayesian hierarchical spatial modeling in disease mapping.
Flexible Bayesian hierarchical spatial modeling in disease mapping.
The Gaussian Intrinsic Conditional Autoregressive (ICAR) spatial model, which usually has two components, namely an ICAR for spatial smoothing and standard random effects for non-s...
Fuzzy Spatial Data Types for Spatial Uncertainty Management in Databases
Fuzzy Spatial Data Types for Spatial Uncertainty Management in Databases
Spatial database systems and geographical information systems are currently only able to support geographical applications that deal with crisp spatial objects, that is, objects wh...
On alleviating spatial confounding issues with the Bayesian Lasso
On alleviating spatial confounding issues with the Bayesian Lasso
Abstract
Spatial confounding usually refers to an issue that often arises in the context of fitting a spatial linear mixed model: the collinearity between the fixed effects...
BIOLOGICAL CONTROL: A QUICK LOOK IN THE ADVANCES OVER 30 YEARS
BIOLOGICAL CONTROL: A QUICK LOOK IN THE ADVANCES OVER 30 YEARS
Biological control is still quite debatable and questioned, especially by producers who still do not see it as a viable and cheap alternative to the control of various types of dis...

