Skip navigation
Por favor, use este identificador para citar o enlazar este ítem: http://rid.unrn.edu.ar/handle/20.500.12049/4059

Registro completo de metadatos
Campo DC Valor Lengua/Idioma
dc.contributor.authorOddi, Facundo José-
dc.contributor.authorMiguez, Fernando E.-
dc.contributor.authorGhermandi, Luciana-
dc.contributor.authorBianchi, Lucas Osvaldo-
dc.contributor.authorGaribaldi, Lucas Alejandro-
dc.date.accessioned2020-01-21T13:37:06Z-
dc.date.available2020-01-21T13:37:06Z-
dc.date.issued2019-08-15-
dc.identifier.citationOddi, 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); 10225-10240es_ES
dc.identifier.issn2045-7758es_ES
dc.identifier.otherhttps://onlinelibrary.wiley.com/doi/full/10.1002/ece3.5543-
dc.identifier.urihttps://rid.unrn.edu.ar/jspui/handle/20.500.12049/4059-
dc.format.extentp. 10225-10240es_ES
dc.format.mediumimpresoes_ES
dc.format.mediumdigitales_ES
dc.language.isoenes_ES
dc.publisherJohn Wiley and Sons Ltdes_ES
dc.titleA nonlinear mixed‐effects modeling approach for ecological data: Using temporal dynamics of vegetation moisture as an examplees_ES
dc.typeArticuloes_ES
dc.rights.licensehttps://creativecommons.org/licenses/by-nc-sa/4.0/es_ES
dc.description.filiationFil: Oddi, Facundo J. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.es_ES
dc.description.filiationFil: Oddi, Facundo J. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.es_ES
dc.description.filiationFil: Miguez, Fernando E. Iowa State University. Department of Agronomy; Estados Unidos.es_ES
dc.description.filiationFil: Ghermandi, Luciana. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.es_ES
dc.description.filiationFil: Ghermandi, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.es_ES
dc.description.filiationFil: Bianchi, Lucas O. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.es_ES
dc.description.filiationFil: Bianchi, Lucas O. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.es_ES
dc.description.filiationFil: Garibaldi, Lucas Alejandro. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.es_ES
dc.description.filiationFil: Garibaldi, Lucas Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.es_ES
dc.subject.keywordCorrelation Structureses_ES
dc.subject.keywordHierarchical Modelinges_ES
dc.subject.keywordNonlinearityes_ES
dc.subject.keywordSpatio‐Temporal Variabilityes_ES
dc.subject.keywordTime Serieses_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.origin.lugarDesarrolloUniversidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural.es_ES
dc.origin.lugarDesarrolloConsejo Nacional de Investigaciones Científicas y Técnica. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural.es_ES
dc.relation.journalissue9 (18)es_ES
dc.description.reviewtruees_ES
dc.description.resumenIncreasingly, 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.es_ES
dc.identifier.doihttps://doi.org/10.1002/ece3.5543-
dc.relation.journalTitleEcology and Evolutiones_ES
Aparece en las colecciones: Artículos


Este documento es resultado del financiamiento otorgado por el Estado Nacional, por lo tanto queda sujeto al cumplimiento de la Ley N° 26.899