國立虎尾科技大學 |

Modelling insect populations in agricultural landscapes

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Modelling insect populations in agricultural landscapes/ edited by Rafael A. Moral, Wesley A.C. Godoy.
other author:	A. Moral, Rafael.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xv, 238 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Insect populations - Mathematical models. -
Online resource:	https://doi.org/10.1007/978-3-031-43098-5
ISBN:	9783031430985

Modelling insect populations in agricultural landscapes
Modelling insect populations in agricultural landscapes[electronic resource] /edited by Rafael A. Moral, Wesley A.C. Godoy. - Cham :Springer International Publishing :2023. - xv, 238 p. :ill. (some col.), digital ;24 cm. - Entomology in focus,v. 82405-8548 ;. - Entomology in focus ;v.3..

Introduction -- Introducing different modelling scenarios to entomologists -- Monte Carlo simulations to model the behaviour of agricultural pests and their natural enemies -- Movement Ecology -- Transition models applied to interactions involving agricultural pests -- Spatial Agent-Based Model With Rules Inspired In Game-Theory: Cases In Insect Resistance Management -- Pest biocontrol and Allee effects acting on the control agent population: Insights from predator-prey models -- On Matrix Stability and Ecological Models -- Machine Vision Applied to Entomology -- Bayesian N-Mixture Models Applied to Estimating Insect Abundance -- Tools for Assessing Goodness-of-fit of GLMs: Case Studies in Entomology.

This book combines chapters emphasising mathematical, statistical, and computational modelling applied to insect populations, particularly pests or natural enemies in agricultural landscapes. There is a gap between agricultural pest experimentation and ecological theory, which requires a connection to supply models with laboratory, and field estimates and projects receiving inputs and insights from models. In addition, decision-making in entomology with respect to pest management and biological conservation of natural enemies has been supported by results obtained from different computational and mathematical approaches. This book brings contemporary issues related to optimization in spatially structured landscapes, insect movement, stability analysis, game theory, machine learning, computer vision, Bayesian modelling, as well as other frameworks.

ISBN: 9783031430985

Standard No.: 10.1007/978-3-031-43098-5doiSubjects--Topical Terms:

1436137
Insect populations
--Mathematical models.

LC Class. No.: QL496.15

Dewey Class. No.: 595.71788

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505 0 $a Introduction -- Introducing different modelling scenarios to entomologists -- Monte Carlo simulations to model the behaviour of agricultural pests and their natural enemies -- Movement Ecology -- Transition models applied to interactions involving agricultural pests -- Spatial Agent-Based Model With Rules Inspired In Game-Theory: Cases In Insect Resistance Management -- Pest biocontrol and Allee effects acting on the control agent population: Insights from predator-prey models -- On Matrix Stability and Ecological Models -- Machine Vision Applied to Entomology -- Bayesian N-Mixture Models Applied to Estimating Insect Abundance -- Tools for Assessing Goodness-of-fit of GLMs: Case Studies in Entomology.
520 $a This book combines chapters emphasising mathematical, statistical, and computational modelling applied to insect populations, particularly pests or natural enemies in agricultural landscapes. There is a gap between agricultural pest experimentation and ecological theory, which requires a connection to supply models with laboratory, and field estimates and projects receiving inputs and insights from models. In addition, decision-making in entomology with respect to pest management and biological conservation of natural enemies has been supported by results obtained from different computational and mathematical approaches. This book brings contemporary issues related to optimization in spatially structured landscapes, insect movement, stability analysis, game theory, machine learning, computer vision, Bayesian modelling, as well as other frameworks.
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650 2 4 $a Landscape Ecology. $3 668603
650 2 4 $a Mathematical and Computational Biology. $3 786706
650 2 4 $a Applied Ecology. $3 668453
700 1 $a A. Moral, Rafael. $3 1436135
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