Estimation and analysis of insect population dynamics parameters via physiologically based models and hybrid genetic algorithm MCMC methods

Autores
Rossini, Luca; Bruzzone, Octavio Augusto; Speranza, Stefano; Delfino, Ines
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Decision support systems are gaining importance in several fields of agriculture, forest, and ecological systems management. Their predictive potential, entrusted to mathematical models, is of fundamental importance to set up opportune strategies to control pests and adversities that may occur and that may seriously compromise the natural equilibria. Among the others, population dynamics is one of the crucial challenges in the field. Despite the scientific community in recent years providing valuable models that faithfully represent terrestrial arthropods populations, such as insects, one of the main concerns is still represented by the parameter estimation. Parameters, in fact, characterise the species and their estimation are often entrusted to dedicated laboratory experiments that require specific equipment and highly qualified personnel. In this study we propose a novel method to estimate the model parameters directly from field data, where experimental activities are less expensive and less time consuming. In this study we propose a combination of least squares methods via genetic algorithms to preliminary evaluate the best parameter values and Markov Chain Monte Carlo approach to obtain their distribution. The algorithm has been tested in the special case of Drosophila suzukii, to quantify part of the parameters of an almost validated model in two steps: i) a first pseudo-validation using perturbed numerical solutions, and ii) a validation using real field data. The results highlighted the potentialities of the algorithm in estimating model parameters and opened several perspectives for further improvements from both the computational and experimental point of view.
EEA Bariloche
Fil: Rossini, Luca. Université Libre de Bruxelles. Service d'Automatique et d'Analyse des Systèmes; Bélgica
Fil: Rossini, Luca. Università degli Studi della Tuscia. Dipartimento di Scienze Agrarie e Forestali; Italia
Fil: Bruzzone, Octavio Augusto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Bruzzone, Octavio Augusto. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Speranza, Stefano. Università degli Studi della Tuscia. Dipartimento di Scienze Agrarie e Forestali; Italia
Fil: Delfino, Ines. Università degli Studi della Tuscia. Dipartimento di Scienze Ecologiche e Biologiche; Italia
Fuente
Ecological Informatics 77 : 102232. (November 2023)
Materia
Insecta
Dinámica de Poblaciones
Modelos
Genética
Algoritmos
Population Dynamics
Models
Genetics
Algorithms
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/14849
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oai_identifier_str oai:localhost:20.500.12123/14849
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spelling Estimation and analysis of insect population dynamics parameters via physiologically based models and hybrid genetic algorithm MCMC methodsRossini, LucaBruzzone, Octavio AugustoSperanza, StefanoDelfino, InesInsectaDinámica de PoblacionesModelosGenéticaAlgoritmosPopulation DynamicsModelsGeneticsAlgorithmsDecision support systems are gaining importance in several fields of agriculture, forest, and ecological systems management. Their predictive potential, entrusted to mathematical models, is of fundamental importance to set up opportune strategies to control pests and adversities that may occur and that may seriously compromise the natural equilibria. Among the others, population dynamics is one of the crucial challenges in the field. Despite the scientific community in recent years providing valuable models that faithfully represent terrestrial arthropods populations, such as insects, one of the main concerns is still represented by the parameter estimation. Parameters, in fact, characterise the species and their estimation are often entrusted to dedicated laboratory experiments that require specific equipment and highly qualified personnel. In this study we propose a novel method to estimate the model parameters directly from field data, where experimental activities are less expensive and less time consuming. In this study we propose a combination of least squares methods via genetic algorithms to preliminary evaluate the best parameter values and Markov Chain Monte Carlo approach to obtain their distribution. The algorithm has been tested in the special case of Drosophila suzukii, to quantify part of the parameters of an almost validated model in two steps: i) a first pseudo-validation using perturbed numerical solutions, and ii) a validation using real field data. The results highlighted the potentialities of the algorithm in estimating model parameters and opened several perspectives for further improvements from both the computational and experimental point of view.EEA BarilocheFil: Rossini, Luca. Université Libre de Bruxelles. Service d'Automatique et d'Analyse des Systèmes; BélgicaFil: Rossini, Luca. Università degli Studi della Tuscia. Dipartimento di Scienze Agrarie e Forestali; ItaliaFil: Bruzzone, Octavio Augusto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Bruzzone, Octavio Augusto. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Speranza, Stefano. Università degli Studi della Tuscia. Dipartimento di Scienze Agrarie e Forestali; ItaliaFil: Delfino, Ines. Università degli Studi della Tuscia. Dipartimento di Scienze Ecologiche e Biologiche; ItaliaElsevier2023-08-01T17:11:59Z2023-08-01T17:11:59Z2023-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/14849https://www.sciencedirect.com/science/article/pii/S15749541230026131574-95411878-0512https://doi.org/10.1016/j.ecoinf.2023.102232Ecological Informatics 77 : 102232. (November 2023)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2026-01-08T10:39:06Zoai:localhost:20.500.12123/14849instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2026-01-08 10:39:06.341INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Estimation and analysis of insect population dynamics parameters via physiologically based models and hybrid genetic algorithm MCMC methods
title Estimation and analysis of insect population dynamics parameters via physiologically based models and hybrid genetic algorithm MCMC methods
spellingShingle Estimation and analysis of insect population dynamics parameters via physiologically based models and hybrid genetic algorithm MCMC methods
Rossini, Luca
Insecta
Dinámica de Poblaciones
Modelos
Genética
Algoritmos
Population Dynamics
Models
Genetics
Algorithms
title_short Estimation and analysis of insect population dynamics parameters via physiologically based models and hybrid genetic algorithm MCMC methods
title_full Estimation and analysis of insect population dynamics parameters via physiologically based models and hybrid genetic algorithm MCMC methods
title_fullStr Estimation and analysis of insect population dynamics parameters via physiologically based models and hybrid genetic algorithm MCMC methods
title_full_unstemmed Estimation and analysis of insect population dynamics parameters via physiologically based models and hybrid genetic algorithm MCMC methods
title_sort Estimation and analysis of insect population dynamics parameters via physiologically based models and hybrid genetic algorithm MCMC methods
dc.creator.none.fl_str_mv Rossini, Luca
Bruzzone, Octavio Augusto
Speranza, Stefano
Delfino, Ines
author Rossini, Luca
author_facet Rossini, Luca
Bruzzone, Octavio Augusto
Speranza, Stefano
Delfino, Ines
author_role author
author2 Bruzzone, Octavio Augusto
Speranza, Stefano
Delfino, Ines
author2_role author
author
author
dc.subject.none.fl_str_mv Insecta
Dinámica de Poblaciones
Modelos
Genética
Algoritmos
Population Dynamics
Models
Genetics
Algorithms
topic Insecta
Dinámica de Poblaciones
Modelos
Genética
Algoritmos
Population Dynamics
Models
Genetics
Algorithms
dc.description.none.fl_txt_mv Decision support systems are gaining importance in several fields of agriculture, forest, and ecological systems management. Their predictive potential, entrusted to mathematical models, is of fundamental importance to set up opportune strategies to control pests and adversities that may occur and that may seriously compromise the natural equilibria. Among the others, population dynamics is one of the crucial challenges in the field. Despite the scientific community in recent years providing valuable models that faithfully represent terrestrial arthropods populations, such as insects, one of the main concerns is still represented by the parameter estimation. Parameters, in fact, characterise the species and their estimation are often entrusted to dedicated laboratory experiments that require specific equipment and highly qualified personnel. In this study we propose a novel method to estimate the model parameters directly from field data, where experimental activities are less expensive and less time consuming. In this study we propose a combination of least squares methods via genetic algorithms to preliminary evaluate the best parameter values and Markov Chain Monte Carlo approach to obtain their distribution. The algorithm has been tested in the special case of Drosophila suzukii, to quantify part of the parameters of an almost validated model in two steps: i) a first pseudo-validation using perturbed numerical solutions, and ii) a validation using real field data. The results highlighted the potentialities of the algorithm in estimating model parameters and opened several perspectives for further improvements from both the computational and experimental point of view.
EEA Bariloche
Fil: Rossini, Luca. Université Libre de Bruxelles. Service d'Automatique et d'Analyse des Systèmes; Bélgica
Fil: Rossini, Luca. Università degli Studi della Tuscia. Dipartimento di Scienze Agrarie e Forestali; Italia
Fil: Bruzzone, Octavio Augusto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Bruzzone, Octavio Augusto. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Speranza, Stefano. Università degli Studi della Tuscia. Dipartimento di Scienze Agrarie e Forestali; Italia
Fil: Delfino, Ines. Università degli Studi della Tuscia. Dipartimento di Scienze Ecologiche e Biologiche; Italia
description Decision support systems are gaining importance in several fields of agriculture, forest, and ecological systems management. Their predictive potential, entrusted to mathematical models, is of fundamental importance to set up opportune strategies to control pests and adversities that may occur and that may seriously compromise the natural equilibria. Among the others, population dynamics is one of the crucial challenges in the field. Despite the scientific community in recent years providing valuable models that faithfully represent terrestrial arthropods populations, such as insects, one of the main concerns is still represented by the parameter estimation. Parameters, in fact, characterise the species and their estimation are often entrusted to dedicated laboratory experiments that require specific equipment and highly qualified personnel. In this study we propose a novel method to estimate the model parameters directly from field data, where experimental activities are less expensive and less time consuming. In this study we propose a combination of least squares methods via genetic algorithms to preliminary evaluate the best parameter values and Markov Chain Monte Carlo approach to obtain their distribution. The algorithm has been tested in the special case of Drosophila suzukii, to quantify part of the parameters of an almost validated model in two steps: i) a first pseudo-validation using perturbed numerical solutions, and ii) a validation using real field data. The results highlighted the potentialities of the algorithm in estimating model parameters and opened several perspectives for further improvements from both the computational and experimental point of view.
publishDate 2023
dc.date.none.fl_str_mv 2023-08-01T17:11:59Z
2023-08-01T17:11:59Z
2023-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/14849
https://www.sciencedirect.com/science/article/pii/S1574954123002613
1574-9541
1878-0512
https://doi.org/10.1016/j.ecoinf.2023.102232
url http://hdl.handle.net/20.500.12123/14849
https://www.sciencedirect.com/science/article/pii/S1574954123002613
https://doi.org/10.1016/j.ecoinf.2023.102232
identifier_str_mv 1574-9541
1878-0512
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Ecological Informatics 77 : 102232. (November 2023)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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score 13.113929

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