Neural Computing and Applications, ( ISI ), Volume (35), No (30), Year (2023-9) , Pages (1-14)

Title : ( Modeling reproductive fitness of predator, Hippodamia variegata (Coleoptera: Coccinellidae) using support vector machine (SVM) on three nitrogen treatments )

Authors: Sayyede Afsane Hosseini , Mojtaba Hosseini , Abbas Rohani , Shaneka Lawson ,

Access to full-text not allowed by authors

Citation: BibTeX | EndNote

Abstract

Protein and carbohydrate content in the diet of the predator Hippodamia variegata Goeze (variegated ladybug) directly influences reproduction fitness by affecting foraging efficiency. The effect of three varied qualities of Aphis gossypii Glover (cotton aphid) prey on daily H. variegata feeding and daily egg production (DEP) were estimated using the support vector machine (SVM). The SVM can predict predator reproductive behavior as a function of the relationship between foraging and prey nutritional composition. We used the total number and weight of aphids consumed, volume of protein, lipid, carbohydrate, and glycogen, and total energy received by the ladybug after feeding on one prey item as input variables. Aphid quality varied as nitrogen (N) fertilization levels were 110, 160, and 210 ppm on the Cucumis sativus L. (cucumber) host plants. The model estimated female beetles consumed more aphids and nutrients on C. sativus plants with N levels of 160 ppm compared to lower (110 ppm) N levels and had higher reproductive transformation efficiencies. Transformation rates of aphid feeding to egg production in females exposed to the 160 ppm treatment were 65% greater, had lower nutrient and energy requirements, and achieved a 29.4% higher DEP than those exposed to high (210 ppm) N levels. The SVM predicted nutrient compositions of A. gossypii exposed to 160 ppm N were balanced such that H. variegata exhibited greater reproduction efficiency than the other N treatments for first 30 d from the start of reproduction

Keywords

Genetic algorithm; Predation efficiency; Predator reproduction efficiency; Prey nutritional content; Support Vector Machine (SVM)
برای دانلود از شناسه و رمز عبور پرتال پویا استفاده کنید.

@article{paperid:1095628,
author = {Hosseini, Sayyede Afsane and Hosseini, Mojtaba and Rohani, Abbas and Shaneka Lawson},
title = {Modeling reproductive fitness of predator, Hippodamia variegata (Coleoptera: Coccinellidae) using support vector machine (SVM) on three nitrogen treatments},
journal = {Neural Computing and Applications},
year = {2023},
volume = {35},
number = {30},
month = {September},
issn = {0941-0643},
pages = {1--14},
numpages = {13},
keywords = {Genetic algorithm; Predation efficiency; Predator reproduction efficiency; Prey nutritional content; Support Vector Machine (SVM)},
}

[Download]

%0 Journal Article
%T Modeling reproductive fitness of predator, Hippodamia variegata (Coleoptera: Coccinellidae) using support vector machine (SVM) on three nitrogen treatments
%A Hosseini, Sayyede Afsane
%A Hosseini, Mojtaba
%A Rohani, Abbas
%A Shaneka Lawson
%J Neural Computing and Applications
%@ 0941-0643
%D 2023

[Download]