Artificial neural networks: A novel approach to analysing the nutritional ecology of a blowfly species, Chrysomya megacephala

André Bianconi1a*, Cláudio J. Von Zuben2b, Adriane B. de S. Serapião3c, José S. Govone3d

1Departamento de Botânica, Instituto de Biociências – Unesp – São Paulo State University, Cep 13506-900, Avenida 24-A, 1515, Bela Vista, Rio Claro-SP, Brazil
2Departamento de Zoologia, IB, Unesp, Rio Claro-SP, Brazil
3Departamento de Estatística, MatemáticaAplicada e Computação, DEMAC, IGCE, Unesp, Rio Claro-SP, Brazil


Bionomic features of blowflies may be clarified and detailed by the deployment of appropriate modelling techniques such as artificial neural networks, which are mathematical tools widely applied to the resolution of complex biological problems. The principal aim of this work was to use three well-known neural networks, namely Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Adaptive Neural Network-Based Fuzzy Inference System (ANFIS), to ascertain whether these tools would be able to outperform a classical statistical method (multiple linear regression) in the prediction of the number of resultant adults (survivors) of experimental populations of Chrysomya megacephala (F.) (Diptera: Calliphoridae), based on initial larval density (number of larvae), amount of available food, and duration of immature stages. The coefficient of determination (R2) derived from the RBF was the lowest in the testing subset in relation to the other neural networks, even though its R2 in the training subset exhibited virtually a maximum value. The ANFIS model permitted the achievement of the best testing performance. Hence this model was deemed to be more effective in relation to MLP and RBF for predicting the number of survivors. All three networks outperformed the multiple linear regression, indicating that neural models could be taken as feasible techniques for predicting bionomic variables concerning the nutritional dynamics of blowflies.

Keywords: insect bionomics, larval density, life-history, mass rearing
Abbreviations: ANFIS, Adaptive Neural Network-Based Fuzzy Inference System; MLP, Multi-Layer Perceptron; RBF, Radial Basis Function

Correspondence: a*,,, *Corresponding author
Associate Editor: David Morton was editor of this paper.

Received: 3 November 2008 | Accepted: 4 February 2009 | Published: 9 June 2010

ISSN: 1536-2442 | Volume 10, Number 58

Bianconi A, Von Zuben CJ, Serapião ABS, Govone JS. 2010. Artificial neural networks: A novel approach to analysing the nutritional ecology of a blowfly species, Chrysomya megacephala.. Journal of Insect Science 10:58, available online:

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