Éditeur(s) : HAL CCSD Public Library of Science
Résumé : Tree mortality in tropical forests is a complex ecological process for which modelling approaches need to be improved to better understand, and then predict, the evolution of tree mortality in response to global change. The mortality model introduced here computes an individual probability of dying for each tree in a community. The mortality model uses the ontogenetic stage of the tree because youngest and oldest trees are more likely to die. Functional traits are integrated as proxies of the ecological strategies of the trees to permit generalization among all species in the community. Data used to parametrize the model were collected at Paracou study site, a tropical rain forest in French Guiana, where 20,408 trees have been censused for 18 years. A Bayesian framework was used to select useful covariates and to estimate the model parameters. This framework was developed to deal with sources of uncertainty, including the complexity of the mortality process itself and the field data, especially historical data for which taxonomic determinations were uncertain. Uncertainty about the functional traits was also considered, to maximize the information they contain. Four functional traits were strong predictors of tree mortality: wood density, maximum height, laminar toughness and stem and branch orientation, which together distinguished the light-demanding, fast-growing trees from slow-growing trees with lower mortality rates. Our modelling approach formalizes a complex ecological problem and offers a relevant mathematical framework for tropical ecologists to process similar uncertain data at the community level.
ISSN: 1932-6203

hal-01190561
https://hal.archives-ouvertes.fr/hal-01190561
PRODINRA : 208582
PUBMED : 23675500
DOI : 10.1371/journal.pone.0063678

Éditeur(s) : HAL CCSD European Geosciences Union
Résumé : The fixation of carbon in tropical forests mainly occurs through the production of wood and leaves, both being the principal components of net primary production. Currently field and satellite observations are independently used to describe the forest carbon cycle, but the link between satellite-derived forest phenology and field-derived forest productivity remains opaque. We used a unique combination of a MODIS enhanced vegetation index (EVI) dataset, a wood production model based on climate data and direct litterfall observations at an intra-annual timescale in order to question the synchronism of leaf and wood production in tropical forests. Even though leaf and wood biomass fluxes had the same range (respectively 2.4 +/- 1.4 and 2.2 +/- 0.4 Mg C ha(-1) yr(-1)), they occurred separately in time. EVI increased with leaf renewal at the beginning of the dry season, when solar irradiance was at its maximum. At this time, wood production stopped. At the onset of the rainy season, when new leaves were fully mature and water available again, wood production quickly increased to reach its maximum in less than a month, reflecting a change in carbon allocation from short-lived pools (leaves) to long-lived pools (wood). The time lag between peaks of EVI and wood production (109 days) revealed a substantial decoupling between the leaf renewal assumed to be driven by irradiance and the water-driven wood production. Our work is a first attempt to link EVI data, wood production and leaf phenology at a seasonal timescale in a tropical evergreen rainforest and pave the way to develop more sophisticated global carbon cycle models in tropical forests.
ISSN: 1726-4170

hal-01268497
https://hal.archives-ouvertes.fr/hal-01268497
DOI : 10.5194/bg-10-7307-2013
PRODINRA : 256018

Éditeur(s) : HAL CCSD Public Library of Science
Résumé : Climate models for the coming century predict rainfall reduction in the Amazonian region, including change in water availability for tropical rainforests. Here, we test the extent to which climate variables related to water regime, temperature and irradiance shape the growth trajectories of neotropical trees. . We developed a diameter growth model explicitly designed to work with asynchronous climate and growth data. Growth trajectories of 205 individual trees from 54 neotropical species censused every 2 months over a 4-year period were used to rank 9 climate variables and find the best predictive model. . About 9% of the individual variation in tree growth was imputable to the seasonal variation of climate. Relative extractable water was the main predictor and alone explained more than 60% of the climate effect on tree growth, i.e. 5.4% of the individual variation in tree growth. Furthermore, the global annual tree growth was more dependent on the diameter increment at the onset of the rain season than on the duration of dry season. . The best predictive model included 3 climate variables: relative extractable water, minimum temperature and irradiance. The root mean squared error of prediction (0.035 mm.d(-1)) was slightly above the mean value of the growth (0.026 mm.d(-1)). . Amongst climate variables, we highlight the predominant role of water availability in determining seasonal variation in tree growth of neotropical forest trees and the need to include these relationships in forest simulators to test, in silico, the impact of different climate scenarios on the future dynamics of the rainforest.
ISSN: 1932-6203

hal-01268350
https://hal.archives-ouvertes.fr/hal-01268350
DOI : 10.1371/journal.pone.0034074
PRODINRA : 215438
PUBMED : 22506012