000			02802nam a2200337 i 4500
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003			ES-MaUEC
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007			cr nn 008mamaa
008			240131s2023 sz | fo |||| 0|eng d
020			_a9783031430985
024	7		_a10.1007/978-3-031-43098-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQL496.15 _b2023 EB
245	0	0	_aModelling Insect Populations in Agricultural Landscapes _cedited by Rafael A. Moral, Wesley A. C. Godoy
250			_a1st ed. 2023
264		1	_aCham _bSpringer International Publishing _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aEntomology in Focus _x2405-8548 _v8
505	0		_aIntroduction -- 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			_aThis 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.
988			_aSpringer_BiomedLife_2023
650		7	_2embne _9137904 _aInsectos _xPoblaciones _xModelos matemáticos
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-43098-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2024 _dz _eb _zSI