Estimation and analysis of insect population dynamics parameters via physiologically based models and hybrid genetic algorithm MCMC methods
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 t...
| Autores principales: | , , , |
|---|---|
| Formato: | info:ar-repo/semantics/artículo |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2023
|
| Materias: | |
| Acceso en línea: | 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 |
| _version_ | 1855037194301865984 |
|---|---|
| author | Rossini, Luca Bruzzone, Octavio Augusto Speranza, Stefano Delfino, Ines |
| author_browse | Bruzzone, Octavio Augusto Delfino, Ines Rossini, Luca Speranza, Stefano |
| author_facet | Rossini, Luca Bruzzone, Octavio Augusto Speranza, Stefano Delfino, Ines |
| author_sort | Rossini, Luca |
| collection | INTA Digital |
| 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. |
| format | info:ar-repo/semantics/artículo |
| id | INTA14849 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INTA148492023-08-01T17:15:06Z Estimation and analysis of insect population dynamics parameters via physiologically based models and hybrid genetic algorithm MCMC methods Rossini, Luca Bruzzone, Octavio Augusto Speranza, Stefano Delfino, Ines Insecta Dinámica de Poblaciones Modelos Genética Algoritmos Population Dynamics Models Genetics Algorithms 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 2023-08-01T17:11:59Z 2023-08-01T17:11:59Z 2023-11 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 eng 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) application/pdf Elsevier Ecological Informatics 77 : 102232. (November 2023) |
| spellingShingle | Insecta Dinámica de Poblaciones Modelos Genética Algoritmos Population Dynamics Models Genetics Algorithms Rossini, Luca Bruzzone, Octavio Augusto Speranza, Stefano Delfino, Ines 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 |
| 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_short | 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 |
| topic | Insecta Dinámica de Poblaciones Modelos Genética Algoritmos Population Dynamics Models Genetics Algorithms |
| 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 |
| work_keys_str_mv | AT rossiniluca estimationandanalysisofinsectpopulationdynamicsparametersviaphysiologicallybasedmodelsandhybridgeneticalgorithmmcmcmethods AT bruzzoneoctavioaugusto estimationandanalysisofinsectpopulationdynamicsparametersviaphysiologicallybasedmodelsandhybridgeneticalgorithmmcmcmethods AT speranzastefano estimationandanalysisofinsectpopulationdynamicsparametersviaphysiologicallybasedmodelsandhybridgeneticalgorithmmcmcmethods AT delfinoines estimationandanalysisofinsectpopulationdynamicsparametersviaphysiologicallybasedmodelsandhybridgeneticalgorithmmcmcmethods |