Estimating tropical tree diversity indices from forestry surveys: A method to integrate taxonomic uncertainty

Guitet, Stéphane; Sabatier, Daniel; Brunaux, Olivier; Hérault, Bruno; Aubry‐Kientz, Mélaine; Molino, Jean‐François; Baraloto, Christopher

doi:10.1016/j.foreco.2014.05.045

Cited by 24 publications

(22 citation statements)

References 49 publications

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“…The reliability of data from forestry surveys in Amazonia has been challenged due to the ambiguity of vernacular names and complex tree taxonomy (Lacerda & Nimmo , Guitet et al . ). For instance, Baraloto et al .…”

Section: Discussionmentioning

confidence: 97%

Using remote sensing to model tree species distribution in Peruvian lowland Amazonia

2018

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Species distribution models for Amazonian trees have mostly been produced at scales and resolutions that are too broad and coarse for practical use in either conservation or forestry. On the other hand, several studies have shown that elevation and the medium‐resolution remote sensing data available via Landsat imagery can be successfully used to detect differences in plant species composition in Amazonia. Therefore, it seems likely that the same data can also be used to predict geographical distributions of individual taxa. Here we use remotely sensed data and a maximum entropy algorithm (MaxEnt) to generate landscape‐scale distribution models at 30‐m‐resolution for five economically important timber tree genera (Apuleia, Amburana, Crepidospermum, Dipteryx, and Manilkara). Individual Landsat Thematic Mapper bands and normalized difference vegetation index yielded acceptable model performance, and the use of averaging filters (3 × 3 and 5 × 5 pixel low‐pass filters) improved model performance further. Including elevation as a predictor also improved model performance for all the genera. Our results suggest that it is possible to use Landsat bands and elevation as predictors for modeling the potential distribution of tree species in lowland Amazonia at a fine enough resolution to facilitate the practical management of forest resources.

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Section: Discussionmentioning

confidence: 97%

Using remote sensing to model tree species distribution in Peruvian lowland Amazonia

2018

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“…Hence, a large number of the trees that died during the study period (1991–2011) have no botanical determination, only a vernacular name (Guitet et al. ).…”

Section: Methodsmentioning

confidence: 99%

“…The botanical determination of the trees was completed in 2012, following extensive inventories with voucher collections and determination at regional and international herbaria. Hence, a large number of the trees that died during the study period have no botanical determination, only a vernacular name (Guitet et al 2014).…”

Section: Datamentioning

confidence: 99%

A joint individual‐based model coupling growth and mortality reveals that tree vigor is a key component of tropical forest dynamics

Aubry‐Kientz

Rossi

Boreux

et al. 2015

Ecology and Evolution

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Tree vigor is often used as a covariate when tree mortality is predicted from tree growth in tropical forest dynamic models, but it is rarely explicitly accounted for in a coherent modeling framework. We quantify tree vigor at the individual tree level, based on the difference between expected and observed growth. The available methods to join nonlinear tree growth and mortality processes are not commonly used by forest ecologists so that we develop an inference methodology based on an MCMC approach, allowing us to sample the parameters of the growth and mortality model according to their posterior distribution using the joint model likelihood. We apply our framework to a set of data on the 20-year dynamics of a forest in Paracou, French Guiana, taking advantage of functional trait-based growth and mortality models already developed independently. Our results showed that growth and mortality are intimately linked and that the vigor estimator is an essential predictor of mortality, highlighting that trees growing more than expected have a far lower probability of dying. Our joint model methodology is sufficiently generic to be used to join two longitudinal and punctual linked processes and thus may be applied to a wide range of growth and mortality models. In the context of global changes, such joint models are urgently needed in tropical forests to analyze, and then predict, the effects of the ongoing changes on the tree dynamics in hyperdiverse tropical forests.

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“…More than 700 woody species attaining 2 cm DBH (diameter at breast height) have been described at the site, with 150 -210 species of trees > 10 cm DBH per hectare. The floristic composition is typical of Guianan rainforests with dominant families including Leguminoseae, Chrysobalanaceae, Lecythidaceae, Sapotaceae and Burseraceae (Guitet et al 2014). In 1984, nine 6.25 ha plots, each one divided into 4 subplots of 1.56 ha each, were established for a complete inventory of all trees > 10 cm DBH.…”

Section: Study Sitementioning

confidence: 99%

Key drivers of ecosystem recovery after disturbance in a neotropical forest

Hérault

Piponiot

2018

For. Ecosyst.

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Background: Natural disturbance is a fundamental component of the functioning of tropical rainforests let to natural dynamics, with tree mortality the driving force of forest renewal. With ongoing global (i.e. land-use and climate) changes, tropical forests are currently facing deep and rapid modifications in disturbance regimes that may hamper their recovering capacity so that developing robust predictive model able to predict ecosystem resilience and recovery becomes of primary importance for decision-making: (i) Do regenerating forests recover faster than mature forests given the same level of disturbance? (ii) Is the local topography an important predictor of the post-disturbance forest trajectories? (iii) Is the community functional composition, assessed with community weighted-mean functional traits, a good predictor of carbon stock recovery? (iv) How important is the climate stress (seasonal drought and/or soil water saturation) in shaping the recovery trajectory? Methods: Paracou is a large scale forest disturbance experiment set up in 1984 with nine 6.25 ha plots spanning on a large disturbance gradient where 15 to 60% of the initial forest ecosystem biomass were removed. More than 70,000 trees belonging to ca. 700 tree species have then been censused every 2 years up today. Using this unique dataset, we aim at deciphering the endogenous (forest structure and composition) and exogenous (local environment and climate stress) drivers of ecosystem recovery in time. To do so, we disentangle carbon recovery into demographic processes (recruitment, growth, mortality fluxes) and cohorts (recruited trees, survivors). Results: Variations in the pre-disturbance forest structure or in local environment do not shape significantly the ecosystem recovery rates. Variations in the pre-disturbance forest composition and in the post-disturbance climate significantly change the forest recovery trajectory. Pioneer-rich forests have slower recovery rates than assemblages of late-successional species. Soil water saturation during the wet season strongly impedes ecosystem recovery but not seasonal drought. From a sensitivity analysis, we highlight the pre-disturbance forest composition and the post-disturbance climate conditions as the primary factors controlling the recovery trajectory. Conclusions: Highly-disturbed forests and secondary forests because they are composed of a lot of pioneer species will be less able to cope with new disturbance. In the context of increasing tree mortality due to both (i) severe droughts imputable to climate change and (ii) human-induced perturbations, tropical forest management should focus on reducing disturbances by developing Reduced Impact Logging techniques.

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Estimating tropical tree diversity indices from forestry surveys: A method to integrate taxonomic uncertainty

Cited by 24 publications

References 49 publications

Using remote sensing to model tree species distribution in Peruvian lowland Amazonia

Using remote sensing to model tree species distribution in Peruvian lowland Amazonia

A joint individual‐based model coupling growth and mortality reveals that tree vigor is a key component of tropical forest dynamics

Key drivers of ecosystem recovery after disturbance in a neotropical forest

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