Autocorrelation offsets zero-inflation in models of tropical saplings density
Modelling the local density of tropical saplings can provide insights into the ecological processes that drive species regeneration and thereby help predict population recovery after disturbance. Yet, few studies have addressed the challenging issues in autocorrelation and zero-inflation of local density. This paper presents Hierarchical Bayesian Modelling (HBM) of sapling density that includes these two features. Special attention is devoted to variable selection, model estimation and comparison. We developed a Zero-Inflated Poisson (ZIP) model with a latent correlated spatial structure and compared it with non-spatial ZIP and Poisson models that were either autocorrelated (Spatial Generalized Linear Mixed, SGLM) or not (generalized linear models, GLM). In our spatial models, local density autocorrelation was modeled by a Conditional Auto-Regressive (CAR) process. 13 explicative variables described ecological conditions with respect to topography, disturbance, stand structure and intraspecific processes. Models were applied to six tropical tree species with differing biological attributes: Oxandra asbeckii, Eperua falcata, Eperua grandiflora, Dicorynia guianensis, Qualea rosea, and Tachigali melinonii. We built species-specific models using a simple method of variable selection based on a latent binary indicator. Our spatial models showed a close correlation between observed and estimated densities with site spatial structure being correctly reproduced. By contrast, the non-spatial models showed poor fits. Variable selection highlighted species-specific requirements and susceptibility to local conditions. Model comparison overall showed that the SGLMwas the most accurate explanatory and predictive model. Surprisingly, zero-inflated models performed less well. Although the SZIP modelwas relevant with respect to data distribution, and more flexible with respect to response curves, its model complexity caused marked variability in parameter estimates. In the SGLM, the spatial process alone accounted for zero-inflation in the data. A refinement of the hypotheses employed at the process level could compensate for distribution flaws at the data level. This study emphasized the importance of the HBM framework in improving the modelling of density-environment relationships.
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Subjects: | U10 - Informatique, mathématiques et statistiques, K01 - Foresterie - Considérations générales, P01 - Conservation de la nature et ressources foncières, modèle mathématique, régénération naturelle, zone tropicale, espacement, arbre, Fabaceae, Eperua falcata, Eperua, Annonaceae, http://aims.fao.org/aos/agrovoc/c_24199, http://aims.fao.org/aos/agrovoc/c_5090, http://aims.fao.org/aos/agrovoc/c_7979, http://aims.fao.org/aos/agrovoc/c_7272, http://aims.fao.org/aos/agrovoc/c_7887, http://aims.fao.org/aos/agrovoc/c_4256, http://aims.fao.org/aos/agrovoc/c_34614, http://aims.fao.org/aos/agrovoc/c_33349, http://aims.fao.org/aos/agrovoc/c_456, http://aims.fao.org/aos/agrovoc/c_3093, http://aims.fao.org/aos/agrovoc/c_3081, |
Online Access: | http://agritrop.cirad.fr/549470/ http://agritrop.cirad.fr/549470/1/document_549470.pdf |
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U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales P01 - Conservation de la nature et ressources foncières modèle mathématique régénération naturelle zone tropicale espacement arbre Fabaceae Eperua falcata Eperua Annonaceae http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_5090 http://aims.fao.org/aos/agrovoc/c_7979 http://aims.fao.org/aos/agrovoc/c_7272 http://aims.fao.org/aos/agrovoc/c_7887 http://aims.fao.org/aos/agrovoc/c_4256 http://aims.fao.org/aos/agrovoc/c_34614 http://aims.fao.org/aos/agrovoc/c_33349 http://aims.fao.org/aos/agrovoc/c_456 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales P01 - Conservation de la nature et ressources foncières modèle mathématique régénération naturelle zone tropicale espacement arbre Fabaceae Eperua falcata Eperua Annonaceae http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_5090 http://aims.fao.org/aos/agrovoc/c_7979 http://aims.fao.org/aos/agrovoc/c_7272 http://aims.fao.org/aos/agrovoc/c_7887 http://aims.fao.org/aos/agrovoc/c_4256 http://aims.fao.org/aos/agrovoc/c_34614 http://aims.fao.org/aos/agrovoc/c_33349 http://aims.fao.org/aos/agrovoc/c_456 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 |
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U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales P01 - Conservation de la nature et ressources foncières modèle mathématique régénération naturelle zone tropicale espacement arbre Fabaceae Eperua falcata Eperua Annonaceae http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_5090 http://aims.fao.org/aos/agrovoc/c_7979 http://aims.fao.org/aos/agrovoc/c_7272 http://aims.fao.org/aos/agrovoc/c_7887 http://aims.fao.org/aos/agrovoc/c_4256 http://aims.fao.org/aos/agrovoc/c_34614 http://aims.fao.org/aos/agrovoc/c_33349 http://aims.fao.org/aos/agrovoc/c_456 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales P01 - Conservation de la nature et ressources foncières modèle mathématique régénération naturelle zone tropicale espacement arbre Fabaceae Eperua falcata Eperua Annonaceae http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_5090 http://aims.fao.org/aos/agrovoc/c_7979 http://aims.fao.org/aos/agrovoc/c_7272 http://aims.fao.org/aos/agrovoc/c_7887 http://aims.fao.org/aos/agrovoc/c_4256 http://aims.fao.org/aos/agrovoc/c_34614 http://aims.fao.org/aos/agrovoc/c_33349 http://aims.fao.org/aos/agrovoc/c_456 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 Flores, Olivier Rossi, Vivien Mortier, Frédéric Autocorrelation offsets zero-inflation in models of tropical saplings density |
description |
Modelling the local density of tropical saplings can provide insights into the ecological processes that drive species regeneration and thereby help predict population recovery after disturbance. Yet, few studies have addressed the challenging issues in autocorrelation and zero-inflation of local density. This paper presents Hierarchical Bayesian Modelling (HBM) of sapling density that includes these two features. Special attention is devoted to variable selection, model estimation and comparison. We developed a Zero-Inflated Poisson (ZIP) model with a latent correlated spatial structure and compared it with non-spatial ZIP and Poisson models that were either autocorrelated (Spatial Generalized Linear Mixed, SGLM) or not (generalized linear models, GLM). In our spatial models, local density autocorrelation was modeled by a Conditional Auto-Regressive (CAR) process. 13 explicative variables described ecological conditions with respect to topography, disturbance, stand structure and intraspecific processes. Models were applied to six tropical tree species with differing biological attributes: Oxandra asbeckii, Eperua falcata, Eperua grandiflora, Dicorynia guianensis, Qualea rosea, and Tachigali melinonii. We built species-specific models using a simple method of variable selection based on a latent binary indicator. Our spatial models showed a close correlation between observed and estimated densities with site spatial structure being correctly reproduced. By contrast, the non-spatial models showed poor fits. Variable selection highlighted species-specific requirements and susceptibility to local conditions. Model comparison overall showed that the SGLMwas the most accurate explanatory and predictive model. Surprisingly, zero-inflated models performed less well. Although the SZIP modelwas relevant with respect to data distribution, and more flexible with respect to response curves, its model complexity caused marked variability in parameter estimates. In the SGLM, the spatial process alone accounted for zero-inflation in the data. A refinement of the hypotheses employed at the process level could compensate for distribution flaws at the data level. This study emphasized the importance of the HBM framework in improving the modelling of density-environment relationships. |
format |
article |
topic_facet |
U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales P01 - Conservation de la nature et ressources foncières modèle mathématique régénération naturelle zone tropicale espacement arbre Fabaceae Eperua falcata Eperua Annonaceae http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_5090 http://aims.fao.org/aos/agrovoc/c_7979 http://aims.fao.org/aos/agrovoc/c_7272 http://aims.fao.org/aos/agrovoc/c_7887 http://aims.fao.org/aos/agrovoc/c_4256 http://aims.fao.org/aos/agrovoc/c_34614 http://aims.fao.org/aos/agrovoc/c_33349 http://aims.fao.org/aos/agrovoc/c_456 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 |
author |
Flores, Olivier Rossi, Vivien Mortier, Frédéric |
author_facet |
Flores, Olivier Rossi, Vivien Mortier, Frédéric |
author_sort |
Flores, Olivier |
title |
Autocorrelation offsets zero-inflation in models of tropical saplings density |
title_short |
Autocorrelation offsets zero-inflation in models of tropical saplings density |
title_full |
Autocorrelation offsets zero-inflation in models of tropical saplings density |
title_fullStr |
Autocorrelation offsets zero-inflation in models of tropical saplings density |
title_full_unstemmed |
Autocorrelation offsets zero-inflation in models of tropical saplings density |
title_sort |
autocorrelation offsets zero-inflation in models of tropical saplings density |
url |
http://agritrop.cirad.fr/549470/ http://agritrop.cirad.fr/549470/1/document_549470.pdf |
work_keys_str_mv |
AT floresolivier autocorrelationoffsetszeroinflationinmodelsoftropicalsaplingsdensity AT rossivivien autocorrelationoffsetszeroinflationinmodelsoftropicalsaplingsdensity AT mortierfrederic autocorrelationoffsetszeroinflationinmodelsoftropicalsaplingsdensity |
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dig-cirad-fr-5494702024-01-28T17:07:23Z http://agritrop.cirad.fr/549470/ http://agritrop.cirad.fr/549470/ Autocorrelation offsets zero-inflation in models of tropical saplings density. Flores Olivier, Rossi Vivien, Mortier Frédéric. 2009. Ecological Modelling, 220 (15) : 1797-1809.https://doi.org/10.1016/j.ecolmodel.2009.01.030 <https://doi.org/10.1016/j.ecolmodel.2009.01.030> Autocorrelation offsets zero-inflation in models of tropical saplings density Flores, Olivier Rossi, Vivien Mortier, Frédéric eng 2009 Ecological Modelling U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales P01 - Conservation de la nature et ressources foncières modèle mathématique régénération naturelle zone tropicale espacement arbre Fabaceae Eperua falcata Eperua Annonaceae http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_5090 http://aims.fao.org/aos/agrovoc/c_7979 http://aims.fao.org/aos/agrovoc/c_7272 http://aims.fao.org/aos/agrovoc/c_7887 http://aims.fao.org/aos/agrovoc/c_4256 http://aims.fao.org/aos/agrovoc/c_34614 http://aims.fao.org/aos/agrovoc/c_33349 http://aims.fao.org/aos/agrovoc/c_456 Guyane française France http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 Modelling the local density of tropical saplings can provide insights into the ecological processes that drive species regeneration and thereby help predict population recovery after disturbance. Yet, few studies have addressed the challenging issues in autocorrelation and zero-inflation of local density. This paper presents Hierarchical Bayesian Modelling (HBM) of sapling density that includes these two features. Special attention is devoted to variable selection, model estimation and comparison. We developed a Zero-Inflated Poisson (ZIP) model with a latent correlated spatial structure and compared it with non-spatial ZIP and Poisson models that were either autocorrelated (Spatial Generalized Linear Mixed, SGLM) or not (generalized linear models, GLM). In our spatial models, local density autocorrelation was modeled by a Conditional Auto-Regressive (CAR) process. 13 explicative variables described ecological conditions with respect to topography, disturbance, stand structure and intraspecific processes. Models were applied to six tropical tree species with differing biological attributes: Oxandra asbeckii, Eperua falcata, Eperua grandiflora, Dicorynia guianensis, Qualea rosea, and Tachigali melinonii. We built species-specific models using a simple method of variable selection based on a latent binary indicator. Our spatial models showed a close correlation between observed and estimated densities with site spatial structure being correctly reproduced. By contrast, the non-spatial models showed poor fits. Variable selection highlighted species-specific requirements and susceptibility to local conditions. Model comparison overall showed that the SGLMwas the most accurate explanatory and predictive model. Surprisingly, zero-inflated models performed less well. Although the SZIP modelwas relevant with respect to data distribution, and more flexible with respect to response curves, its model complexity caused marked variability in parameter estimates. In the SGLM, the spatial process alone accounted for zero-inflation in the data. A refinement of the hypotheses employed at the process level could compensate for distribution flaws at the data level. This study emphasized the importance of the HBM framework in improving the modelling of density-environment relationships. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/549470/1/document_549470.pdf application/pdf Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1016/j.ecolmodel.2009.01.030 10.1016/j.ecolmodel.2009.01.030 http://catalogue-bibliotheques.cirad.fr/cgi-bin/koha/opac-detail.pl?biblionumber=195136 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ecolmodel.2009.01.030 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1016/j.ecolmodel.2009.01.030 |