TY  - BOOK
AU  - A.Moral,Rafael
AU  - Godoy,Wesley A.C.
ED  - SpringerLink (Online service)
TI  - Modelling Insect Populations in Agricultural Landscapes
T2  - Entomology in Focus,
SN  - 9783031430985
AV  - S1-972 
U1  - 630 23
PY  - 2023///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Agriculture
KW  - Animal culture
KW  - Landscape ecology
KW  - Biomathematics
KW  - Applied ecology
KW  - Animal Science
KW  - Landscape Ecology
KW  - Mathematical and Computational Biology
KW  - Applied Ecology
N1  - Acceso multiusuario; 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
N2  - 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
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-43098-5
ER  -