Éditeur(s) : HAL CCSD Wiley
Résumé : International audience
1. Estimating forest above-ground biomass (AGB), or carbon (AGC), in tropical forests has become a majorconcern for scientists and stakeholders. However, AGB assessment procedures are not fully standardized andeven more importantly the uncertainty associated with AGB estimates is seldom assessed.2. Here, we present an R package designed to compute both AGB/AGC estimate and its associated uncertaintyfrom forest plot datasets, using a Bayesian inference procedure. The package builds upon previous work onpantropical and regional biomass allometric equations and published datasets by default but it can alsointegrate unpublished or complementary datasets in many steps.3. BIOMASS performs a number of standard tasks on input forest tree inventories: i) tree speciesidentification, if available, is automatically corrected; ii) wood density is estimated from tree species identity;iii) if height data are available, a local height-diameter allometry may be built; else height is inferred frompantropical or regional models; iv) finally, AGB/AGC are estimated by propagating the errors associated withall the calculation steps up to the final estimate. R code is given in the paper and in the appendix forillustration purpose.4. The BIOMASS package should contribute to improved standards for AGB calculation for tropical foreststands, and will encourage users to report the uncertainties associated with stand-level AGB/AGC estimatesin future studies.
ISSN: 2041-210X

hal-01558180
https://hal.archives-ouvertes.fr/hal-01558180
DOI : 10.1111/2041-210X.12753

Éditeur(s) : HAL CCSD INRIA
Résumé : International audience
Computational probabilistic modeling and Bayesian inference has met a great success over the past fifteen years through the development of Monte Carlo methods and the ever increasing performance of computers. Through methods such as Monte Carlo Markov chain and sequential Monte Carlo Bayesian inference effectively combines with Markovian modelling. This approach has been very successful in ecology and agronomy. We analyze the development of this approach applied to a few examples of natural resources management.
La modélisation probabiliste et l'inférence bayésienne computationnelles rencontrent un très grand succès depuis une quinzaine d'années grâce au développement des méthodes de Monte Carlo et aux performances toujours croissantes des moyens de calcul. Au travers d'outils comme les méthodes de Monte Carlo par chaîne de Markov et les méthodes de Monte Carlo séquentielles, l'inférence bayésienne se combine efficacement à la modélisation markovienne. Cette approche est également très répendue dans le domaine de l'écologie et l'agronomie. Nous faisons le point sur les développements de cette approche appliquée à quelques exemples de gestion de ressources naturelles.
ARIMA

inria-00506383
https://hal.inria.fr/inria-00506383
https://hal.inria.fr/inria-00506383/document

Éditeur(s) : HAL CCSD Elsevier Masson
Résumé :  
Modeling soil water availability for tropical trees is a prerequisite to predicting the future impact of climate change on tropical forests. In this paper we develop a discrete-time deterministic water balance model adapted to tropical rainforest climates, and we validate it on a large dataset that includes micrometeorological and soil parameters along a topographic gradient in a lowland forest of French Guiana. The model computes daily water fluxes (rainfall interception, drainage, tree transpiration and soil plus understorey evapotranspiration) and soil water content using three input variables: daily precipitation, potential evapotranspiration and solar radiation. A novel statistical approach is employed that uses Time Domain Reflectometer (TDR) soil moisture data to estimate water content at permanent wilting point and at field capacity, and root distribution. Inaccuracy of the TDR probes and other sources of uncertainty are taken into account by model calibration through a Bayesian framework. Model daily output includes relative extractable water, REW, i.e. the daily available water standardized by potential available water. The model succeeds in capturing temporal variations in REW regardless of topographic context. The low Root Mean Square Error of Predictions suggests that the model captures the most important drivers of soil water dynamics, i.e. water refilling and root water extraction. Our model thus provides a useful tool to explore the response of tropical forests to climate scenarios of changing rainfall regime and intensity.
ISSN: 0168-1923

hal-01032181
https://hal.archives-ouvertes.fr/hal-01032181
DOI : 10.1016/j.agrformet.2011.04.012

Éditeur(s) : HAL CCSD Wiley
Résumé :
How Quaternary climatic and geological disturbances influenced the composition of Neotropical forests is hotly debated. Rain fall and temperature changes during and/or immediately after the Last Glacial Maximum (LGM) are thought to have strongly affected the geographical distribution and local abundance of tree species. The paucity of the fossil records in Neotropical forests prevents a direct reconstruction of such processes. To describe community-level historical trends in forest composition, we turned therefore to inferential methods based on the reconstruction of past demographic changes. In particular, we modelled the history of rainforests in the Eastern Guiana Shield over a time scale of several thousand generations, through the application of Approximate Bayesian Computation (ABC) and Maximum Likelihood (ML) methods to diversity data at nuclear and chloroplast loci in eight species or subspecies of rainforest trees. Depending on the species and on the method applied, we detected population contraction, expansion or stability, with a general trend in favour of stability or expansion, with changes presumably having occurred during or after the LGM. These findings suggest that Guiana Shield rain forests have globally persisted, while expanding, through the Quaternary, but that different species have experienced different demographic events, with a trend towards the increase in frequency of light-demanding, disturbance-associated species.
ISSN: 0962-1083

hal-01512150
https://hal.archives-ouvertes.fr/hal-01512150
https://hal.archives-ouvertes.fr/hal-01512150/document
https://hal.archives-ouvertes.fr/hal-01512150/file/Barthe_et_al-_%7B0B6A8C5E-DDFF-4C4E-822C-4E7BC22A6C9B%7D.pdf
DOI : 10.1111/mec.13949