A nonlinear mixed‐effects modeling approach for ecological data: Using temporal dynamics of vegetation moisture as an example

Autores
Oddi, Facundo José; Miguez, Fernando E.; Ghermandi, Luciana; Bianchi, Lucas Osvaldo; Garibaldi, Lucas Alejandro
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Fil: Oddi, Facundo J. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.
Fil: Oddi, Facundo J. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.
Fil: Miguez, Fernando E. Iowa State University. Department of Agronomy; Estados Unidos.
Fil: Ghermandi, Luciana. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.
Fil: Ghermandi, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.
Fil: Bianchi, Lucas O. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.
Fil: Bianchi, Lucas O. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.
Fil: Garibaldi, Lucas Alejandro. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.
Fil: Garibaldi, Lucas Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.
Increasingly, often ecologist collects data with nonlinear trends, heterogeneous variances, temporal correlation, and hierarchical structure. Nonlinear mixed‐effects models offer a flexible approach to such data, but the estimation and interpretation of these models present challenges, partly associated with the lack of worked examples in the ecological literature. We illustrate the nonlinear mixed‐effects modeling approach using temporal dynamics of vegetation moisture with field data from northwestern Patagonia. This is a Mediterranean‐type climate region where modeling temporal changes in live fuel moisture content are conceptually relevant (ecological theory) and have practical implications (fire management). We used this approach to answer whether moisture dynamics varies among functional groups and aridity conditions, and compared it with other simpler statistical models. The modeling process is set out “step‐by‐step”: We start translating the ideas about the system dynamics to a statistical model, which is made increasingly complex in order to include different sources of variability and correlation structures. We provide guidelines and R scripts (including a new self‐starting function) that make data analyses reproducible. We also explain how to extract the parameter estimates from the R output. Our modeling approach suggests moisture dynamic to vary between grasses and shrubs, and between grasses facing different aridity conditions. Compared to more classical models, the nonlinear mixed‐effects model showed greater goodness of fit and met statistical assumptions. While the mixed‐effects approach accounts for spatial nesting, temporal dependence, and variance heterogeneity; the nonlinear function allowed to model the seasonal pattern. Parameters of the nonlinear mixed‐effects model reflected relevant ecological processes. From an applied perspective, the model could forecast the time when fuel moisture becomes critical to fire occurrence. Due to the lack of worked examples for nonlinear mixed‐effects models in the literature, our modeling approach could be useful to diverse ecologists dealing with complex data.
Materia
Correlation Structures
Hierarchical Modeling
Nonlinearity
Spatio‐Temporal Variability
Time Series
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
RID-UNRN (UNRN)
Institución
Universidad Nacional de Río Negro
OAI Identificador
oai:rid.unrn.edu.ar:20.500.12049/4059

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network_acronym_str RIDUNRN
repository_id_str 4369
network_name_str RID-UNRN (UNRN)
spelling A nonlinear mixed‐effects modeling approach for ecological data: Using temporal dynamics of vegetation moisture as an exampleOddi, Facundo JoséMiguez, Fernando E.Ghermandi, LucianaBianchi, Lucas OsvaldoGaribaldi, Lucas AlejandroCorrelation StructuresHierarchical ModelingNonlinearitySpatio‐Temporal VariabilityTime SeriesFil: Oddi, Facundo J. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.Fil: Oddi, Facundo J. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.Fil: Miguez, Fernando E. Iowa State University. Department of Agronomy; Estados Unidos.Fil: Ghermandi, Luciana. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.Fil: Ghermandi, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.Fil: Bianchi, Lucas O. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.Fil: Bianchi, Lucas O. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.Fil: Garibaldi, Lucas Alejandro. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.Fil: Garibaldi, Lucas Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.Increasingly, often ecologist collects data with nonlinear trends, heterogeneous variances, temporal correlation, and hierarchical structure. Nonlinear mixed‐effects models offer a flexible approach to such data, but the estimation and interpretation of these models present challenges, partly associated with the lack of worked examples in the ecological literature. We illustrate the nonlinear mixed‐effects modeling approach using temporal dynamics of vegetation moisture with field data from northwestern Patagonia. This is a Mediterranean‐type climate region where modeling temporal changes in live fuel moisture content are conceptually relevant (ecological theory) and have practical implications (fire management). We used this approach to answer whether moisture dynamics varies among functional groups and aridity conditions, and compared it with other simpler statistical models. The modeling process is set out “step‐by‐step”: We start translating the ideas about the system dynamics to a statistical model, which is made increasingly complex in order to include different sources of variability and correlation structures. We provide guidelines and R scripts (including a new self‐starting function) that make data analyses reproducible. We also explain how to extract the parameter estimates from the R output. Our modeling approach suggests moisture dynamic to vary between grasses and shrubs, and between grasses facing different aridity conditions. Compared to more classical models, the nonlinear mixed‐effects model showed greater goodness of fit and met statistical assumptions. While the mixed‐effects approach accounts for spatial nesting, temporal dependence, and variance heterogeneity; the nonlinear function allowed to model the seasonal pattern. Parameters of the nonlinear mixed‐effects model reflected relevant ecological processes. From an applied perspective, the model could forecast the time when fuel moisture becomes critical to fire occurrence. Due to the lack of worked examples for nonlinear mixed‐effects models in the literature, our modeling approach could be useful to diverse ecologists dealing with complex data.John Wiley and Sons Ltd2019-08-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfOddi, Facundo J., Miguez, Fernando E., Ghermandi, Luciana., Bianchi, Lucas O. y Garibaldi, Lucas Alejandro (2019). John Wiley and Sons Ltd; Ecology and Evolution; 9 (18); 10225-102402045-7758https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.5543https://rid.unrn.edu.ar/jspui/handle/20.500.12049/4059https://doi.org/10.1002/ece3.5543eng9 (18)Ecology and Evolutioninfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/4.0/reponame:RID-UNRN (UNRN)instname:Universidad Nacional de Río Negro2025-10-16T10:05:43Zoai:rid.unrn.edu.ar:20.500.12049/4059instacron:UNRNInstitucionalhttps://rid.unrn.edu.ar/jspui/Universidad públicaNo correspondehttps://rid.unrn.edu.ar/oai/snrdrid@unrn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:43692025-10-16 10:05:43.24RID-UNRN (UNRN) - Universidad Nacional de Río Negrofalse
dc.title.none.fl_str_mv A nonlinear mixed‐effects modeling approach for ecological data: Using temporal dynamics of vegetation moisture as an example
title A nonlinear mixed‐effects modeling approach for ecological data: Using temporal dynamics of vegetation moisture as an example
spellingShingle A nonlinear mixed‐effects modeling approach for ecological data: Using temporal dynamics of vegetation moisture as an example
Oddi, Facundo José
Correlation Structures
Hierarchical Modeling
Nonlinearity
Spatio‐Temporal Variability
Time Series
title_short A nonlinear mixed‐effects modeling approach for ecological data: Using temporal dynamics of vegetation moisture as an example
title_full A nonlinear mixed‐effects modeling approach for ecological data: Using temporal dynamics of vegetation moisture as an example
title_fullStr A nonlinear mixed‐effects modeling approach for ecological data: Using temporal dynamics of vegetation moisture as an example
title_full_unstemmed A nonlinear mixed‐effects modeling approach for ecological data: Using temporal dynamics of vegetation moisture as an example
title_sort A nonlinear mixed‐effects modeling approach for ecological data: Using temporal dynamics of vegetation moisture as an example
dc.creator.none.fl_str_mv Oddi, Facundo José
Miguez, Fernando E.
Ghermandi, Luciana
Bianchi, Lucas Osvaldo
Garibaldi, Lucas Alejandro
author Oddi, Facundo José
author_facet Oddi, Facundo José
Miguez, Fernando E.
Ghermandi, Luciana
Bianchi, Lucas Osvaldo
Garibaldi, Lucas Alejandro
author_role author
author2 Miguez, Fernando E.
Ghermandi, Luciana
Bianchi, Lucas Osvaldo
Garibaldi, Lucas Alejandro
author2_role author
author
author
author
dc.subject.none.fl_str_mv Correlation Structures
Hierarchical Modeling
Nonlinearity
Spatio‐Temporal Variability
Time Series
topic Correlation Structures
Hierarchical Modeling
Nonlinearity
Spatio‐Temporal Variability
Time Series
dc.description.none.fl_txt_mv Fil: Oddi, Facundo J. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.
Fil: Oddi, Facundo J. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.
Fil: Miguez, Fernando E. Iowa State University. Department of Agronomy; Estados Unidos.
Fil: Ghermandi, Luciana. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.
Fil: Ghermandi, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.
Fil: Bianchi, Lucas O. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.
Fil: Bianchi, Lucas O. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.
Fil: Garibaldi, Lucas Alejandro. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.
Fil: Garibaldi, Lucas Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.
Increasingly, often ecologist collects data with nonlinear trends, heterogeneous variances, temporal correlation, and hierarchical structure. Nonlinear mixed‐effects models offer a flexible approach to such data, but the estimation and interpretation of these models present challenges, partly associated with the lack of worked examples in the ecological literature. We illustrate the nonlinear mixed‐effects modeling approach using temporal dynamics of vegetation moisture with field data from northwestern Patagonia. This is a Mediterranean‐type climate region where modeling temporal changes in live fuel moisture content are conceptually relevant (ecological theory) and have practical implications (fire management). We used this approach to answer whether moisture dynamics varies among functional groups and aridity conditions, and compared it with other simpler statistical models. The modeling process is set out “step‐by‐step”: We start translating the ideas about the system dynamics to a statistical model, which is made increasingly complex in order to include different sources of variability and correlation structures. We provide guidelines and R scripts (including a new self‐starting function) that make data analyses reproducible. We also explain how to extract the parameter estimates from the R output. Our modeling approach suggests moisture dynamic to vary between grasses and shrubs, and between grasses facing different aridity conditions. Compared to more classical models, the nonlinear mixed‐effects model showed greater goodness of fit and met statistical assumptions. While the mixed‐effects approach accounts for spatial nesting, temporal dependence, and variance heterogeneity; the nonlinear function allowed to model the seasonal pattern. Parameters of the nonlinear mixed‐effects model reflected relevant ecological processes. From an applied perspective, the model could forecast the time when fuel moisture becomes critical to fire occurrence. Due to the lack of worked examples for nonlinear mixed‐effects models in the literature, our modeling approach could be useful to diverse ecologists dealing with complex data.
description Fil: Oddi, Facundo J. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.
publishDate 2019
dc.date.none.fl_str_mv 2019-08-15
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 Oddi, Facundo J., Miguez, Fernando E., Ghermandi, Luciana., Bianchi, Lucas O. y Garibaldi, Lucas Alejandro (2019). John Wiley and Sons Ltd; Ecology and Evolution; 9 (18); 10225-10240
2045-7758
https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.5543
https://rid.unrn.edu.ar/jspui/handle/20.500.12049/4059
https://doi.org/10.1002/ece3.5543
identifier_str_mv Oddi, Facundo J., Miguez, Fernando E., Ghermandi, Luciana., Bianchi, Lucas O. y Garibaldi, Lucas Alejandro (2019). John Wiley and Sons Ltd; Ecology and Evolution; 9 (18); 10225-10240
2045-7758
url https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.5543
https://rid.unrn.edu.ar/jspui/handle/20.500.12049/4059
https://doi.org/10.1002/ece3.5543
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 9 (18)
Ecology and Evolution
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv John Wiley and Sons Ltd
publisher.none.fl_str_mv John Wiley and Sons Ltd
dc.source.none.fl_str_mv reponame:RID-UNRN (UNRN)
instname:Universidad Nacional de Río Negro
reponame_str RID-UNRN (UNRN)
collection RID-UNRN (UNRN)
instname_str Universidad Nacional de Río Negro
repository.name.fl_str_mv RID-UNRN (UNRN) - Universidad Nacional de Río Negro
repository.mail.fl_str_mv rid@unrn.edu.ar
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