LoggingLab: An R package to simulate reduced-impact selective logging in tropical forests using forest inventory data - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Journal Articles Ecological Modelling Year : 2024

LoggingLab: An R package to simulate reduced-impact selective logging in tropical forests using forest inventory data

Abstract

Even where Reduced-Impact Logging (RIL) practices are applied, selective logging causes substantial damage to tropical forests. To further reduce selective logging damage, the practices that cause the most damage need to be identified and alternatives tested. To this end, we developed the R package LoggingLab, a spatially-explicit and individual tree-based selective logging simulator and demonstrated its functions using data from French Guiana. LoggingLab explicitly simulates damage during each stage of the selective logging process taking into account topography and hydrography, which are main constraints on logging. Most LoggingLab parameters can be easily adjusted to a wide range of local contexts. LoggingLab can also be coupled with forest dynamics models to simulate the long- term effects of different selective logging scenarios.
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Dates and versions

hal-04286637 , version 1 (15-11-2023)

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Vincyane Badouard, Sylvain Schmitt, Guillaume Salzet, Thomas Gaquiere, Margaux Rojat, et al.. LoggingLab: An R package to simulate reduced-impact selective logging in tropical forests using forest inventory data. Ecological Modelling, 2024, 487, pp.110539. ⟨10.1016/j.ecolmodel.2023.110539⟩. ⟨hal-04286637⟩
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