Biology subjects

Vieilledent, G.

Publications and source records attributed to Vieilledent, G..

9 recordsLinked to original sources

bioRxiv2025

Beyond species means - the intraspecific contribution to global wood density variation

Wood density is central for estimating vegetation carbon storage and a plant functional trait of great ecological and evolutionary importance. However, the global extent of wood density variation is unclear, especially at the intraspecific level. We assembled the most comprehensive wood density collection to date (GWDD v.2), including 109,626 records from 16,829 plant species across woody life forms and biomes. Using the GWDD v.2, we explored the sources of variation in wood density within individuals, within species, and across environmental gradients. Intraspecific variation accounted for up to 15% of overall wood density variation (sd = 0.068 g cm-3). Sapwood densities varied 50% less than heartwood densities, and branchwood densities varied 30% less than trunkwood densities. Individuals in extreme environments (dry, hot, acidic soils) had higher wood density than conspecifics elsewhere (+0.02 g cm-3, [~]4% of the mean). Intraspecific environmental effects strongly tracked interspecific patterns (r = 0.83) but were only 20-30% as large and varied considerably among taxa. Individual plant wood density was difficult to predict (RMSE > 0.08 g cm-3; single-measurement R2 = 0.59). We recommend (i) systematic within-species sampling for local applications, and (ii) expanded taxonomic coverage combined with integrative models for robust estimates across ecological scales.

plant biology↗

bioRxiv2025

A global map of wood density

Wood density influences how quickly woody plants grow, how long they live and how much carbon they store, yet its global variation remains poorly mapped. Here we combined 109,626 wood density measurements from 16,829 species with 300,949 vegetation plots to produce a km-scale map of community-weighted wood density for every woody biome. Our model led to a prediction accuracy 32-51 % higher than previous global products, and a 1.8-3.7-fold wider wood density range (0.28-1.00 g cm-3; global mean: 0.57 g cm-3) than previously assumed. Spatial cross-validation showed low bias ({+/-}2.5 % of the mean), and uncertainties decreased from 20% in poorly sampled drylands and boreal regions to 5% in data-rich temperate forests. Mean annual temperature was the best predictor of community-weighted mean wood density, increasing by 0.01 g cm-3 for every 1{degrees}C change. We deliver a low-bias, high-resolution wood density layer for Earth system models, together with spatially explicit error maps. This study represents a major step forward for carbon accounting and trait-based forecasts of vegetation change.

plant biology↗

bioRxiv2022

Beyond variance: simple random distributions are not a good proxy for intraspecific variability in systems with environmental structure

The role of intraspecific variability (IV) in shaping community dynamics and species coexistence has been intensively discussed over the past decade and modelling studies have played an important role in that respect. However, these studies often implicitly assume that IV can be represented by independent random draws around speciesspecific mean parameters. This major assumption has largely remained undiscussed, although a great part of observed IV is structured in space or time, in particular when environmental dimensions that influence individual performance are imperfectly characterised or unobserved in the field. To test the impact of this strong assumption on the outcome of community dynamics models, we designed a simulation experiment where we varied the level of knowledge of the environment in virtual communities, resulting in different relative importance of explained vs unexplained spatial individual variation in performance. We used a community dynamics simulator to generate communities where the unexplained individual variation is, or is not, added as an unstructured random noise. Communities simulated with unstructured IV never reached the community diversity and composition of those where all the variation was explained and structured (perfect knowledge model). This highlights that incorporating unstructured IV (i.e. a random noise) to account for unexplained (but structured) variation can lead to incorrect simulations of community dynamics. In addition, the effects of unstructured IV on community diversity and composition depended on the relative importance of structured vs unstructured IV, i.e. on the level of knowledge of the environment, which may partly explain the contrasting results of previous studies on the effect of IV on species coexistence. In particular, the effect of unstructured IV on community diversity was positive when the proportion of structured IV vs unstructured IV in the model was low, but negative when this proportion was high. This is because unstructured random noise can either limit the competitive exclusion of inferior competitors in low dimensions or destabilise tight niche partitioning in high dimension. Our study suggests that it is crucial to account for the sources and structure of observed IV in real communities to better understand its effect on community assembly and properly include it in community dynamics models.

ecology↗

bioRxiv2022

Forest refuge areas and carbon emissions from tropical deforestation in the 21st century

Tropical forests are disappearing at an alarming rate due to human activities. Here, we provide spatial models of deforestation in 92 countries covering all the tropical moist forests in the world. Our results question the global effectiveness of protected areas in curbing deforestation and allow reinterpreting the impact of roads on deforestation in terms of both accessibility and forest fragmentation. Using our models, we derived high-resolution pantropical maps of the deforestation risk and future forest cover for the 21st century under a "business-as-usual" scenario based on the deforestation rates observed in the 2010s. Under this scenario, 48% (39-56%) of tropical moist forests are expected to disappear during the course of the 21st century, and 41 tropical countries will have lost all their forests by 2100. The remaining forests in 2100 will be highly fragmented and located in remote places, preferentially in protected areas, far from roads and villages, and at high elevations. We also show that future deforestation will likely concern forests with higher aboveground carbon stocks, and hence that carbon emissions from tropical deforestation are expected to increase (from 0.432-0.585 Pg/yr in 2020 to 0.583-0.628 Pg/yr in 2100). Considering also the decrease in carbon uptake in aboveground biomass (from 0.589 Pg/yr in 2000 to 0.312 Pg/yr in 2100) associated with the decrease in forest cover, tropical moist forests would become a major net carbon source in the 21st century under this scenario.

ecology↗

bioRxiv2022

Rethinking the role of intraspecific variability in species coexistence

ContextIntraspecific variability (IV) has been proposed to explain species coexistence in diverse communities. Assuming, sometimes implicitly, that conspecific individuals can perform differently in the same environment and that IV blurs species differences, previous studies have found contrasting results regarding the effect of IV on species coexistence. ObjectiveWe aim at showing that the large IV observed in data does not mean that conspecific individuals are necessarily different in their response to the environment and that the role of high-dimensional environmental variation in determining IV has been largely underestimated in forest plant communities. Methods and ResultsWe first used a simulation experiment where an individual attribute is derived from a high-dimensional model, representing "perfect knowledge" of individual response to the environment, to illustrate how a large observed IV can result from "imperfect knowledge" of the environment. Second, using growth data from clonal Eucalyptus plantations in Brazil, we estimated a major contribution of the environment in determining individual growth. Third, using tree growth data from long-term tropical forest inventories in French Guiana, Panama and India, we showed that tree growth in tropical forests is structured spatially and that despite a large observed IV at the population level, conspecific individuals perform more similarly locally than compared with heterospecific individuals. SynthesisAs the number of environmental dimensions that are typically quantified is generally much lower than the actual number of environmental dimensions influencing individual attributes, a great part of observed IV might be misinterpreted as random variation across individuals when in fact it is environmentally-driven. This mis-representation has important consequences for inference about community dynamics. We emphasize that observed IV does not necessarily impact species coexistence per se but can reveal species response to high-dimensional environment, which is consistent with niche theory and the observation of the many differences between species in nature.

ecology↗

bioRxiv2020

Long-term (1990-2019) monitoring of tropical moist forests dynamics

Accurate characterization of the tropical moist forests changes is needed to support conservation policies and to better quantify their contribution to global carbon fluxes. We document - at pantropical scale - the extent of these forests and their changes (degradation, deforestation and recovery) over the last three decades. We estimate that 17% of the tropical moist forests have disappeared since 1990 with a remaining area of 1060 million ha in 2019, from which 8.5% are degraded. Our study underlines the importance of the degradation process in such ecosystems, in particular as precursor of deforestation and in the recent increase of the tropical moist forest disturbances. Without reduction of the present disturbance rates, undisturbed forests will disappear entirely in large tropical humid regions by 2050. Our study suggests reinforcing actions to prevent the first disturbance scar that leads to forest clearance in 45% of the cases.

ecology↗

bioRxiv2020

It's not just poverty: unregulated global market and bad governance explain unceasing deforestation in Western Madagascar

Madagascar is recognized both for its unparalleled biodiversity and the high level of threat suffered by this biodiversity, associated in particular with anthropogenic deforestation. Despite sustained efforts to fight poverty and curb deforestation, forest cover in Madagascar is rapidly decreasing. To try to explain why it is so difficult to stop deforestation in Madagascar, we analysed the recent deforestation process in Western Madagascar through satellite image analysis and field surveys. We show that deforestation has increased from less than 0.9%/yr on 2000-2010 to more than 2%/yr on 2010-2017. We identified two major causes of deforestation, which were not associated with subsistence agriculture: slash-and-burn agriculture for the cultivation of cash crops (maize and peanut), and uncontrolled fires to create open pasture. Maize production is mainly at the destination of the domestic market and is used in particular for livestock feeding. Peanut production has boomed since 2013 and more than half of it is now exported towards asiatic countries. The money earned by farmers is principally invested into zebu herd acquisition. Trade of agricultural commodities benefits several intermediaries, some of whom have political responsibilities thus creating conflicts of interest. On the other hand, agents from institutions in charge of the management of the protected areas have no means to enforce laws against deforestation. In the absence of an efficient strategy to stop deforestation, we predicted that 38-93% of the forest present in 2000 will have disappeared in 2050. Forest loss, apart from biodiversity loss and climate-change global issues, will be at the expense of local population. In order to stop deforestation, international aid should be used to improve local governance to enforce environmental laws and pressure should be put on trading companies to buy certified agricultural commodities that are not derived from deforestation.

ecology↗

bioRxiv2018

New formula and conversion factor to compute tree species basic wood density from a global wood technology database.

Premise of the studyBasic wood density is an important ecological trait for woody plants. It is used to characterize species performance and fitness in community ecology, and to compute tree and forest biomass in carbon cycle studies. While wood density has been historically measured at 12% moisture, it is convenient for ecological purposes to convert this measure to basic wood density, i.e. the ratio of dry mass over green volume. Basic wood density can then be used to compute tree dry biomass from living tree volume.\n\nMethodsHere, we derive a new, exact formula to compute the basic wood density Db from the density at moisture content w denoted Dw, the fibre saturation point S, and the volumetric shrinkage coefficient R. We estimated a new conversion factor using a global wood technology database where values to use this formula are available for 4022 trees collected in 64 countries (mostly tropical) and representing 872 species.\n\nKey resultsWe show that previous conversion factors used to convert densities at 12% moisture into basic wood densities are inconsistent. Based on theory and data, we found that basic wood density could be inferred from the density at 12% moisture using the following formula: Db = 0.828D122. This value of 0.828 provides basic wood density estimates 4-5% smaller than values inferred from previous conversion factors.\n\nConclusionsThis new conversion factor should be used to derive basic wood densities in global wood density databases. This would prevent overestimating global forest carbon stocks and allow predicting better tree species community dynamics from wood density.

plant biology↗

bioRxiv2017

Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar

The island of Madagascar has a unique biodiversity, mainly located in the tropical forests of the island. This biodiversity is highly threatened by anthropogenic deforestation. Existing historical forest maps at national level are scattered and have substantial gaps which prevent an exhaustive assessment of long-term deforestation trends in Madagascar. In this study, we combined historical national forest cover maps (covering the period 1953-2000) with a recent global annual tree cover loss dataset (2001-2014) to look at six decades of deforestation and forest fragmentation in Madagascar (from 1953 to 2014). We produced new forest cover maps at 30 m resolution for the year 1990 and annually from 2000 to 2014 over the full territory of Madagascar. We estimated that Madagascar has lost 44% of its natural forest cover over the period 1953-2014 (including 37% over the period 1973-2014). Natural forests cover 8.9 Mha in 2014 (15% of the national territory) and include 4.4 Mha (50%) of moist forests, 2.6 Mha (29%) of dry forests, 1.7 Mha of spiny forests (19%) and 177,000 ha (2%) of mangroves. Since 2005, the annual deforestation rate has progressively increased in Madagascar to reach 99,000 ha/yr during 2010-2014 (corresponding to a rate of 1.1%/yr). Around half of the forest (46%) is now located at less than 100 m from the forest edge. Our approach could be replicated to other developing countries with tropical forest. Accurate forest cover change maps can be used to assess the effectiveness of past and current conservation programs and implement new strategies for the future. In particular, forest maps and estimates can be used in the REDD+ framework which aims at \"Reducing Emissions from Deforestation and forest Degradation\" and for optimizing the current protected area network.

ecology↗