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
- Institución
- Universidad Nacional de Río Negro
- OAI Identificador
- oai:rid.unrn.edu.ar:20.500.12049/4059
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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) |
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RID-UNRN (UNRN) |
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Universidad Nacional de Río Negro |
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RID-UNRN (UNRN) - Universidad Nacional de Río Negro |
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rid@unrn.edu.ar |
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12.712165 |