Data and code of the publication entitled: A common framework to model recovery in disturbed tropical forests
We developed an original Bayesian hierarchical model of recovery trajectories, considering disturbed forests in a common framework, through a disturbance intensity gradient, inferred with the loss of basal area due to disturbance. As a case study, we tested our modelling approach on above-ground biomass, Shannon taxonomic diversity and taxonomic composition similarity from two long-term experiments, Tirimbina (Costa Rica) and Paracou (French Guiana), where forest permanent sample plots have been set up following selective logging (63.25 ha), agriculture (4 ha), and clearcutting+fire (6.25 ha). <br> <strong>This dataset contains:</strong> <ul> <li><strong>1 dictionary data text file</strong></li> <li><strong>5 model scripts (run under the R package rstan version 2.26.13):</strong> <ul> <li>Stan codes for the vegetation attribute predictions: above-ground biomass (AGB), Shannon diversity, and composition similarity.</li> <li>Stan codes for the general models of long-term and long- + short-term processes presented in the publication.</li> </ul> </li> <li><strong>12 data sets:</strong> <ul> <li>Basal area in disturbed forests, and in old-growth forests (for above-ground biomass and Shannon diversity predictions)</li> <li>Above-ground biomass in disturbed forests, and in old-growth forests (for above-ground biomass predictions)</li> <li>Shannon diversity in disturbed forests, and in old-growth forests (for Shannon diversity predictions)</li> <li>Basal area in selectively logged forest, in clearcut+fire forest, and in old-growth forests (for composition similarity predictions)</li> <li>Composition similarity in selectively logged forest, in clearcut+fire forest, and in old-growth forests (for composition similarity predictions)</li> </ul> </li></ul>
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Subjects: | Earth and Environmental Sciences, anthropogenic disturbance, recovery, tropical forest conservation, vegetation attribute trajectories, ecosystem modelling, |
Online Access: | https://doi.org/10.18167/DVN1/8KL5PC |
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dat-cirad-10.18167DVN18KL5PC2023-06-13T00:00:04ZData and code of the publication entitled: A common framework to model recovery in disturbed tropical forestshttps://doi.org/10.18167/DVN1/8KL5PCMaurent, EliottHérault, BrunoPiponiot, CamilleDerroire, GéraldineDelgado, DiegoFinegan, BryanAubry-Kientz, MélaineAmani, Bienvenu H. K.Ngo Bieng, Marie AngeCIRAD DataverseWe developed an original Bayesian hierarchical model of recovery trajectories, considering disturbed forests in a common framework, through a disturbance intensity gradient, inferred with the loss of basal area due to disturbance. As a case study, we tested our modelling approach on above-ground biomass, Shannon taxonomic diversity and taxonomic composition similarity from two long-term experiments, Tirimbina (Costa Rica) and Paracou (French Guiana), where forest permanent sample plots have been set up following selective logging (63.25 ha), agriculture (4 ha), and clearcutting+fire (6.25 ha). <br> <strong>This dataset contains:</strong> <ul> <li><strong>1 dictionary data text file</strong></li> <li><strong>5 model scripts (run under the R package rstan version 2.26.13):</strong> <ul> <li>Stan codes for the vegetation attribute predictions: above-ground biomass (AGB), Shannon diversity, and composition similarity.</li> <li>Stan codes for the general models of long-term and long- + short-term processes presented in the publication.</li> </ul> </li> <li><strong>12 data sets:</strong> <ul> <li>Basal area in disturbed forests, and in old-growth forests (for above-ground biomass and Shannon diversity predictions)</li> <li>Above-ground biomass in disturbed forests, and in old-growth forests (for above-ground biomass predictions)</li> <li>Shannon diversity in disturbed forests, and in old-growth forests (for Shannon diversity predictions)</li> <li>Basal area in selectively logged forest, in clearcut+fire forest, and in old-growth forests (for composition similarity predictions)</li> <li>Composition similarity in selectively logged forest, in clearcut+fire forest, and in old-growth forests (for composition similarity predictions)</li> </ul> </li></ul>Earth and Environmental Sciencesanthropogenic disturbancerecoverytropical forest conservationvegetation attribute trajectoriesecosystem modellingMaurent, Eliott |
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Centre de coopération internationale en recherche agronomique pour le développement |
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Earth and Environmental Sciences anthropogenic disturbance recovery tropical forest conservation vegetation attribute trajectories ecosystem modelling Earth and Environmental Sciences anthropogenic disturbance recovery tropical forest conservation vegetation attribute trajectories ecosystem modelling |
spellingShingle |
Earth and Environmental Sciences anthropogenic disturbance recovery tropical forest conservation vegetation attribute trajectories ecosystem modelling Earth and Environmental Sciences anthropogenic disturbance recovery tropical forest conservation vegetation attribute trajectories ecosystem modelling Maurent, Eliott Hérault, Bruno Piponiot, Camille Derroire, Géraldine Delgado, Diego Finegan, Bryan Aubry-Kientz, Mélaine Amani, Bienvenu H. K. Ngo Bieng, Marie Ange Data and code of the publication entitled: A common framework to model recovery in disturbed tropical forests |
description |
We developed an original Bayesian hierarchical model of recovery trajectories, considering disturbed forests in a common framework, through a disturbance intensity gradient, inferred with the loss of basal area due to disturbance. As a case study, we tested our modelling approach on above-ground biomass, Shannon taxonomic diversity and taxonomic composition similarity from two long-term experiments, Tirimbina (Costa Rica) and Paracou (French Guiana), where forest permanent sample plots have been set up following selective logging (63.25 ha), agriculture (4 ha), and clearcutting+fire (6.25 ha).
<br>
<strong>This dataset contains:</strong>
<ul>
<li><strong>1 dictionary data text file</strong></li>
<li><strong>5 model scripts (run under the R package rstan version 2.26.13):</strong>
<ul>
<li>Stan codes for the vegetation attribute predictions: above-ground biomass (AGB), Shannon diversity, and composition similarity.</li>
<li>Stan codes for the general models of long-term and long- + short-term processes presented in the publication.</li>
</ul>
</li>
<li><strong>12 data sets:</strong>
<ul>
<li>Basal area in disturbed forests, and in old-growth forests (for above-ground biomass and Shannon diversity predictions)</li>
<li>Above-ground biomass in disturbed forests, and in old-growth forests (for above-ground biomass predictions)</li>
<li>Shannon diversity in disturbed forests, and in old-growth forests (for Shannon diversity predictions)</li>
<li>Basal area in selectively logged forest, in clearcut+fire forest, and in old-growth forests (for composition similarity predictions)</li>
<li>Composition similarity in selectively logged forest, in clearcut+fire forest, and in old-growth forests (for composition similarity predictions)</li>
</ul>
</li></ul> |
author2 |
Maurent, Eliott |
author_facet |
Maurent, Eliott Maurent, Eliott Hérault, Bruno Piponiot, Camille Derroire, Géraldine Delgado, Diego Finegan, Bryan Aubry-Kientz, Mélaine Amani, Bienvenu H. K. Ngo Bieng, Marie Ange |
topic_facet |
Earth and Environmental Sciences anthropogenic disturbance recovery tropical forest conservation vegetation attribute trajectories ecosystem modelling |
author |
Maurent, Eliott Hérault, Bruno Piponiot, Camille Derroire, Géraldine Delgado, Diego Finegan, Bryan Aubry-Kientz, Mélaine Amani, Bienvenu H. K. Ngo Bieng, Marie Ange |
author_sort |
Maurent, Eliott |
title |
Data and code of the publication entitled: A common framework to model recovery in disturbed tropical forests |
title_short |
Data and code of the publication entitled: A common framework to model recovery in disturbed tropical forests |
title_full |
Data and code of the publication entitled: A common framework to model recovery in disturbed tropical forests |
title_fullStr |
Data and code of the publication entitled: A common framework to model recovery in disturbed tropical forests |
title_full_unstemmed |
Data and code of the publication entitled: A common framework to model recovery in disturbed tropical forests |
title_sort |
data and code of the publication entitled: a common framework to model recovery in disturbed tropical forests |
publisher |
CIRAD Dataverse |
url |
https://doi.org/10.18167/DVN1/8KL5PC |
work_keys_str_mv |
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