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).
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<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>