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Wood density is an emergent property resultant of tree growth strategies modulated by local edapho-climatic and stand development conditions. It is associated with the biomechanical support of trees and hydraulic conductivity or safety, directly and indirectly influencing a range of ecological processes, including, among others, tree growth, tree resistance to disturbances, and mortality. Tree wood density is also crucial for assessing vegetation carbon stocks by supporting the link between a volumetric retrieval and a mass estimate. Earlier studies based on tree-level wood density measurements have reported significant relationships between wood density, environmental conditions, and tree growth strategies. However, these were either regionally focused or suffering from data availability, lacking a representative large-scale and spatially explicit representation of factors influencing tree wood density. This study collects and collates information from several sources to construct a global database of 28,822 tree-level wood density measurements alongside with a wide set of climate, soils, topography, and Earth observation covariates to support the development of statistical models for wood density. The dataset, consisting of more than 3,000 global covariates, is used for training different machine learning models, including random forest model (RF), light gradient boosting model (LGBM), extreme gradient boosting model (XGBoost), and bagged trees models. The experimental design considers six cross-validation approaches: either random 5-fold; according to two sets of climate classifications, land cover types, ecozones, or latitudinal ranges. Model performances are assessed with the coefficient of determination (R2) and Root-mean-square errors (RMSE) when predicting an independent test subset of wood density. The top ten models show a prominent performance (R2 > 0.67 and RMSE < 0.09), and their ensemble mean, and standard deviation are considered the best estimation and uncertainty in wood density predictions, respectively. Systematic underestimation biases are observed around the low northern latitudes (0º-20ºN), primarily due to the lack of wood density measurements. Further analysis of sources of uncertainties and their quantification support the generation of a global quantitative and spatially explicit representation of wood density. The ecological interpretation and quantitative assessment of global wood density, and associated uncertainties aim to contribute to improving predictions of vegetation biomass and inferring ecosystem resistance under current and future climate scenarios.

Context. Measuring intra-patch connectivity, i.e. the connectivity within a habitat patch, is important to evaluate landscape fragmentation and connectivity. However, intra-patch connectivity is mainly measured with patch size, which can conceal diverse intra-patch connectivity patterns for similar patch size distributions. Objectives. We suggest a method to refine the intra-patch connectivity component of fragmentation and connectivity indices. This method allows for distinguishing different intra-patch connectivity patterns for similar patch size distributions. Methods. We used normalized patch complexity indices to weight patch size in common fragmentation and connectivity indices. Patch complexity indices included two existing geometrical indices (SHAPE and FRAC), and a new index derived from spatial network analysis, the mean detour index (MDI). We analyzed the behaviours of adjusted fragmentation and connectivity indices theoretically and empirically on both artificial and real landscapes. Results. While maintaining the mathematical properties of fragmentation and connectivity indices, our method could distinguish landscapes with identical patch size distributions but different spatial configurations. The mean detour index had a different response than geometrical indices. This result indicates that, at the patch level, topological complexity can exhibit different patterns from geometrical complexity. Conclusions. Measuring intra-patch connectivity with patch size in fragmentation and connectivity indices cannot distinguish landscapes having similar patch sizes distribution but different spatial configurations. This paper introduces a method to distinguish such patterns relying on geometrical and topological indices and shows to which extent it can impact conservation planning.

International audience; 1. Neutral landscape models have many applications in ecology, such as supporting spatially-explicit simulations, developing, and evaluating landscape indices. However, current approaches provide few options to produce large landscapes with controlled composition and fragmentation indices. 2. We introduce flsgen (Fragmented Landscape Generator), a new neutral landscape generator that address this limitation by providing a high level of control over 14 landscape indices. The main novelty of flsgen is the decomposition of landscape generation into two steps: the solving of a constraint satisfaction problem and the generation of a landscape raster with a stochastic algorithm. The latter relies on a continuous environmental gradient that influences the landscape’s spatial configuration. 3. flsgen can generate fine-grained artificial landscapes in small amounts of time, which makes it suited to produce large landscape series systematically. We demonstrate the features of flsgen through three illustrative use cases. 4. flsgen is a practical and efficient tool that expand the current possibilities of neutral landscape models and widen their potential applications. To facilitate its uptake, flsgen is available as free and open-source software through a Java API, a command-line interface, or an R package.

About a third of New Caledonia is covered with ultramafic soils (UM) which hosts a rich flora (endemism > 85%) threatened by mining activities. This combination makes the ultramafic vegetation a floristic hotspot within a biodiversity hotspot. UM soils are distributed from sea level to 1618 m elevation with about two-thirds forming a large continuous unit while the remaining forms numerous relatively small isolated units. Here, we provide a synthesis of the distribution of forest and tree species across 22 UM units. We compiled an extensive tree occurrence dataset (109,896 occurrences and 1,065 species) and a new expert-based forest map at a 1:3000 spatial resolution. Only 10% of these species represented more than 50% of the occurrences, while 10% of the species had only one or two occurrences. A quarter of the UM area did not contain any occurrences, and we estimated that on average a quarter of the species remained to be inventoried in the 22 units. Forest covers about one-third of the UM areas with forest coverage ranging from 1.7% to 72.3% in the different UM units. Forest coverage increased from 14.6% on sea level to 93.3% at 1,200 m of elevation. About 30% of the forest and 90% of the species were located within mining concessions, while 14.5% and 73%, respectively, were located within protected areas. We recommend setting up new protected areas on ultramafic substrate, specially in the Northern province, to protect more forest and the diversity it harbours.

Ecological theory aims to understand how and why species differences allow competitors to coexist, but explanations remain inconsistent with data. Tightly constrained parameter tradeoffs needed for coexistence in models contrast with evidence that forests can support high diversity and be invaded repeatedly by species that lack specialized tradeoffs. By translating environmental responses to individual covariance, a universal coexistence hypothesis shows i) that species differences lead to a natural tendency to concentrate competition within the species, the common feature needed to promote coexistence in models, and ii) the fingerprint of this effect is available in covariances between individuals that can be observed in nature. The many ways in which species differ make high diversity almost inevitable. This covariance not only provides the evidence for this mechanism; it further provides a new direction for earth surface models that currently cannot sustain diverse communities despite large numbers of evidence-based parameters.

International audience; Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement.

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 21stcentury 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 21stcentury, 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 21stcentury under this scenario.

Global change is altering interactions between ecological disturbances. We review interactions between tropical cyclones and fires that affect woody biomes in many islands and coastal areas. Cyclone-induced damage to trees can increase fuel loads on the ground and dryness in the understory, which increases the likelihood, intensity, and area of subsequent fires. In forest biomes, cyclone–fire interactions may initiate a grass–fire cycle and establish stable open-canopy biomes. In cyclone-prone regions, frequent cyclone-enhanced fires may generate and maintain stable open-canopy biomes (e.g., savannas and woodlands). We discuss how global change is transforming fire and cyclone regimes, extensively altering cyclone–fire interactions. These altered cyclone–fire interactions are shifting biomes away from historical states and causing loss of biodiversity Refereed/Peer-reviewed

International audience; How species coexist while competiting for the same resources is a long-standing question in community ecology, particularly for hyperdiverse communities like tropical forests. In the past decades, intraspecific variability (IV) and its ecological consequences became of major interest, and IV was perceived as a potential mechanism enabling species coexistence, with contrasting results in the literature however.We argue that taking the nature of IV into account is important to understand its effects on coexistence, and hypothesise that environmental variation alone can produce IV in performance.Focusing on spatial environmental variation, we build a body of evidence to support this idea. First, we use a theoretical model using virtual data of individual growth across environmental gradients, using less explicative environmental variables in the model than to generate the data. Second we analyse a Eucalyptus clonal dataset in Brazil. Lastly we analyse three datasets from contrasting tropical forests : Paracou in French Guiana (Amazonia), Barro Colorado Island in Panama (Central America) and Uppangala in the Western Ghats in India (Southeast Asia).The theoretical model shows that observed IV can emerge due to the lower dimensionality of field observations compared with the high dimensionality of the environment, without any intrinsic differences between conspecifics; the clonal dataset analysis shows that IV can emerge from exogenous factors; the tropical dataset analyses show that IV in growth is high in tropical forests, that growth is broadly spatially autocorrelated, which we suppose is a mark of its environmental origin, and that locally, conspecifics have a more similar growth than heterospecifics.This body of evidence shows that IV can emerge from environmental variation and without any intrinsic differences between conspecific individuals. Based on theory, which shows that intraspecific competition must be stronger than interspecific competition to enable species coexistence, we link higher local similarity to higher competition and conclude that intraspecific variability does not preclude this condition, enabling multiple species to coexist in high dimensional and spatially structured environments.

Three decades of satellite observations provide unprecedented information about forest change trajectories in the humid tropics., Accurate characterization of tropical moist forest changes is needed to support conservation policies and to quantify their contribution to global carbon fluxes more effectively. We document, at pantropical scale, the extent and changes (degradation, deforestation, and recovery) of these forests over the past three decades. We estimate that 17% of tropical moist forests have disappeared since 1990 with a remaining area of 1071 million hectares in 2019, from which 10% are degraded. Our study underlines the importance of the degradation process in these ecosystems, in particular, as a precursor of deforestation, and in the recent increase in tropical moist forest disturbances (natural and anthropogenic degradation or deforestation). Without a reduction of the present disturbance rates, undisturbed forests will disappear entirely in large tropical humid regions by 2050. Our study suggests that reinforcing actions are needed to prevent the initial degradation that leads to forest clearance in 45% of the cases.

There are many sources of uncertainty in scenarios and models of socio-ecological systems, and understanding these uncertainties is critical in supporting informed decision-making about the management of natural resources. Here, we review uncertainty across the steps needed to create socio-ecological scenarios, from narrative storylines to the representation of human and biological processes in models and the estimation of scenario and model parameters. We find that socio-ecological scenarios and models would benefit from moving away from “stylized” approaches that do not consider a wide range of direct drivers and their dependency on indirect drivers. Indeed, a greater focus on the social phenomena is fundamental in understanding the functioning of nature on a human-dominated planet. There is no panacea for dealing with uncertainty, but several approaches to evaluating uncertainty are still not routinely applied in scenario modeling, and this is becoming increasingly unacceptable. However, it is important to avoid uncertainties becoming an excuse for inaction in decision-making when facing environmental challenges.

International audience; 1. Curbing habitat loss, reducing fragmentation, and restoring connectivity are frequent concerns of conservation planning. In this respect, the incorporation of spatial constraints, fragmentation, and connectivity indices into optimization procedures is an important challenge for improving decision support.2. Here we present a novel optimization approach developed to accurately represent a broad range of conservation planning questions with spatial constraints and landscape indices. Relying on constraint programming, a technique from artificial intelligence based on automatic reasoning, this approach provides both constraint satisfaction and optimality guarantees.3. We applied this approach in a real case study to support managers of the “Côte Ou bliée – ‘Woen Vùù – Pwa Pereeù” provincial park project, in the biodiversity hotspot of New Caledonia. Under budget, accessibility, and equitable allocation constraints, we identified restorable areas optimal for reducing forest fragmentation and improving inter‐patch structural connectivity, respectively measured with the effective mesh size and the integral index of connectivity.4. Synthesis and applications. Our work contributes to more effective and policy‐relevant conservation planning by providing a spatially‐explicit and problem‐focused optimization approach. By allowing an exact representation of spatial constraints and landscape indices, it can address new questions and ensure whether the solutions will be socio‐economically feasible, through optimality and satisfiability guarantees. Our approach is generic and flexible, thus applicable to a wide range of conservation planning problems such as ecological restoration planning, reserve or corridor design.

International audience; People and nature interact since millennia in forests worldwide, but current management strategies addressing these ecosystems often exclude local people from the decision-making process. This top-down approach is the cornerstone of conservation initiatives, particularly in highly threatened and fragmented forested ecosystems. In contrast, collaborative management involving the participation of local communities has increasingly contributed to conservation efforts globally. Here we ask how collaborative management would contribute to the conservation of a threatened, culturally important, and keystone tree species. We address this question in the Araucaria Forest System11Araucaria Forest System – AFS (AFS) in southern Brazil, where the main conservation strategy has been top-down based on restrictive use. Throughout the entire distribution of AFS, we interviewed 97 smallholders about how they use and manage Araucaria angustifolia trees (araucaria). We integrated their Traditional Ecological Knowledge22Traditional Ecological Knowledge – TEK (TEK) with a literature review about the conservation status of Araucaria Forests to analyze potential outcomes of two alternative conservation models: top-down with restrictive use, and bottom-up with collaborative management. We identified the feedback mechanisms in each model, and how they dampen or self-reinforced critical processes for AFS resilience. Our models showed that a top-down strategy maintains forest cover resilient to illegal logging but at the cost of losing TEK (undermining socio-ecological resilience) and forest resilience to other external disturbances, such as climate change. Alternatively, a bottom-up approach based on successful collaborative management schemes may increase the general resilience of AFS, while preserving TEK, thus contributing to maintaining the entire social-ecological system. Our findings indicate how it is paramount to maintain TEK to conserve AFS in the long term through collaborative management. By including local actors in the governance of AFS, its resilience is reinforced, promoting forest expansion, maintenance of TEK, and participatory conservation.

International audience; It is commonly accepted that species should move toward higher elevations and latitudes to track shifting isotherms as climate warms. However, temperature might not be the only limiting factor determining species distribution. Species might move to opposite directions to track changes in other climatic variables. Here, we used an extensive occurrence data set and an ensemble modelling approach to model the climatic niche and to predict the distribution of the seven baobab species (genus Adansonia) present in Madagascar. Using climatic projections from three global circulation models, we predicted species’ future distribution and extinction risk for 2055 and 2085 under two representative concentration pathways (RCPs) and two dispersal scenarios. We disentangled the role of each climatic variable in explaining species range shift looking at relative variable importance and future climatic anomalies. Four baobab species (Adansonia rubrostipa, Adansonia madagascariensis, Adansonia perrieri¸ and Adansonia suarezensis) could experience a severe range contraction in the future (>70% for year 2085 under RCP 8.5, assuming a zero-dispersal hypothesis). For three out of the four threatened species, range contraction was mainly explained by an increase in temperature seasonality, especially in the North of Madagascar, where they are currently distributed. In tropical regions, where species are commonly adapted to low seasonality, we found that temperature seasonality will generally increase. It is, thus, very likely that many species in the tropics will be forced to move equatorward to avoid an increase in temperature seasonality. Yet, several ecological (e.g., equatorial limit, or unsuitable deforested habitat) or geographical barriers (absence of lands) could prevent species to move equatorward, thus increasing the extinction risk of many tropical species, like endemic baobab species in Madagascar.

International audience; The ForestAtRisk Python package can be used to model the spatial probability of deforesta-8tion and predict future forest cover in the tropics. The spatial data used to model deforestation9comes from georeferenced raster files which can be very large (several gigabytes). The func-10tions available in theForestAtRiskpackage process large rasters by blocks of data, making11calculations fast and efficient. This allows deforestation to be modeled over large geographic 12 areas (eg. at the country scale) and at high resolution (eg.30 m). TheForestAtRisk13package offers the possibility of using a logistic regression with auto-correlated spatial random14effects to model the deforestation process. The spatial random effects make it possible to15structure the residual spatial variability in the deforestation process which is not explained by16the variables of the model and is often very large. In addition to providing new features, the17ForestAtRiskPython package is open source (GPLv3 license), cross-platform and scriptable18(via Python), user-friendly (functions provided with full documentation and examples), and19easily extendable (with statistical models for example). The Python package ForestAtRisk20was recently used to model deforestation and predict future forest cover by 2100 across the humid tropics

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.

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 analyzed 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.

Tropical moist forest (TMF) provide essential ecosystem services1,2. Fine-scale mapping and characterization of their disturbances are needed to support global conservation policies3 and to accurately quantify their contribution to global carbon fluxes4. However, limited information exists on their remaining extent and long-term historical changes.We produced a wall-to-wall map of TMF cover dynamics at 30-meter resolution from 1990 to 2019. Each individual image of the full Landsat archive (~1 200 000 scenes) has been mapped using an expert system to allow all disturbances in the forest cover - including from selective logging activities and fires that are visible during a short period - to be depicted and characterized in terms of timing (dates and duration), sequential dynamics, intensity, and extent.The performance of our disturbance classifier has been validated against 12 235 reference sample plots resulting in 9.4% omissions, 8.1% false detections and 91.4% overall accuracy. Our dataset depicts the TMF extent and patterns of disturbances through two complementary layers: a transition map and an annual change dataset. The transition map captures the resulting disturbance dynamics over the 30 years by depicting (i) remaining undisturbed forests, (ii) two types of degraded forests (corresponding mostly to either logged or burned forests), (iii) young forest regrowth, and (iv) deforested land that includes four subcategories of converted land cover: (a) water bodies (new dams and river flow changes); (b) tree plantations; and (c) other land cover that includes infrastructure, agriculture, and mining. The annual change dataset is a collection of 30 maps depicting - for each year between 1990 and 2019 - the spatial extents of undisturbed forests and disturbances.We found that pan-tropical forest disturbances have been underestimated so far. For the first time at this scale, we discriminate deforestation from degradation and we underline the importance of the degradation process in tropical forest ecosystems. Our analysis shows the trends of deforestation and degradation by country, sub-region, and continent. Finally, we extrapolated the recent average rates of disturbances to predict the extent of the undisturbed TMF by 2050.We will continue to update the TMF dataset with future Landsat data and intend to adapt the methodology to Sentinel 2 data (available since 2015) towards near real-time monitoring of TMF with a higher frequency of observations and finer spatial resolution.1. Gibson et al. 2011 doi:10.1038/nature104252. Watson et al. 2018 Doi:10.1038/s41559-018-0490-x3. Mackey et al. 2015 doi:10.1111/conl.121204. Mitchard E.T.A. 2018 doi

Land degradation and regeneration are complex processes that greatly impact climate regulation, ecosystem service provision, and population well-being and require an urgent and appropriate response through land use planning and interventions. Spatially explicit land change models can greatly help decision makers, but traditional regression approaches fail to capture the nonlinearity and complex interactions of the underlying drivers. Our objective was to use a machine learning algorithm combined with high-resolution data sets to provide simultaneous and spatial forecasts of deforestation, land degradation, and regeneration for the next two decades. A 17,000-km(2) region in the south of Madagascar was taken as the study area. First, an empirical analysis of drivers of change was conducted, and then, an ensemble model was calibrated to predict and map potential changes based on 12 potential explanatory variables. These potential change maps were used to draw three scenarios of land change while considering past trends in intensity of change and expert knowledge. Historical observations displayed clear patterns of land degradation and relatively low regeneration. Amongst the 12 potential explanatory variables, distance to forest edge and elevation were the most important for the three land transitions studied. Random forest showed slightly better prediction ability compared with maximum entropy and generalized linear model. Business-as-usual scenarios highlighted the large areas under deforestation and degradation threat, and an alternative scenario enabled the location of suitable areas for regeneration. The approach developed herein and the spatial outputs provided can help stakeholders target their interventions or develop large-scale sustainable land management strategies.

International audience; Reducing carbon emissions from deforestation and degradation (REDD) requires detailed insight into how the forest biomass is measured and distributed. Studies so far have estimated forest biomass stocks using rough assumptions and unreliable data. High-resolution data and robust methods are required to capture the spatial variability of forest biomass with sufficient precision. Here we aim to improve on previous approaches by using radar satellite ALOS PALSAR (25-m resolution) and optical Landsat-derived tree cover (30-m resolution) observations to estimate forest biomass stocks in Madagascar, for the years 2007'2010. The radar signal and in situ biomass were highly correlated (R2 = 0.71) and the root mean square error was 30% (for biomass ranging from 0 to 500 t/ha). Using our map at 25-m resolution for the entire island of Madagascar, we estimated the total above-ground forest carbon for the four years 2007, 2008, 2009 and 2010 to be 1.1173 ± 0.0304, 1.1029 ± 0.0303, 1.0916 ± 0.0301 and 1.0773 ± 0.0298 PgC, respectively. Carbon stocks were found to have decreased constantly over this period due to anthropogenic deforestation and likely also to climate change. The results are expected to serve as a more accurate benchmark for monitoring progress on REDD and to provide strong supports for current and future spaceborne missions such as ALOS-2, SAOCOM and BIOMASS.

The recent conversion of land to oil palm crops has produced catastrophic effects in Southeast Asia. Shocking images of orangutans left with no home, together with health awareness campaigns, have already reduced the consumption of palm oil-based products, and steered food companies towards product lines “palm oil free”. But will this be enough to preserve primates populations?

International audience; Understanding how carbon and biodiversity vary across tropical forest landscapes is essential to achieving effective conservation of their respective hotspots in a global context of high deforestation. Whether conservation strategies aimed at protecting carbon hotspots can provide co-benefits for biodiversity protection, and vice versa, highly depends on the extent to which carbon and biodiversity co-occur at the landscape level. We used field measurements and easily accessible explanatory variables to model aboveground carbon density, soil carbon density and tree alpha diversity (response variables) over a mostly forested area of northern Borneo. We assessed the spatial relationships between response variables and the spatial congruence of their hotspots. We found a significant positive relationship between aboveground carbon density and tree alpha diversity, and an above-than-expected-by-chance spatial congruence of their hotspots. Consequently, the protection of areas of high aboveground carbon density through financial mechanisms such as REDD+ is expected to benefit tree diversity conservation in the study area. On the other hand, relationships between soil carbon density and both aboveground carbon density and tree alpha diversity were negative and spatial congruences null. Hotspots of soil carbon density, mostly located in peatlands, therefore need specific conservation regulations, which the current moratorium on peat conversion in Indonesia is a first step toward.

Summary Recent studies have underlined the importance of climatic variables in determining tree height and biomass in tropical forests. Nonetheless, the effects of climate on tropical forest carbon stocks remain uncertain. In particular, the application of process-based dynamic global vegetation models has led to contrasting conclusions regarding the potential impact of climate change on tropical forest carbon storage. Using a correlative approach based on a bioclimatic envelope model and data from 1771 forest plots inventoried during the period 1996–2013 in Madagascar over a large climatic gradient, we show that temperature seasonality, annual precipitation and mean annual temperature are key variables in determining forest above-ground carbon density. Taking into account the explicative climate variables, we obtained an accurate (R2 = 70% and RMSE = 40 Mg ha−1) forest carbon map for Madagascar at 250 m resolution for the year 2010. This national map was more accurate than previously published global carbon maps (R2 ≤ 26% and RMSE ≥ 63 Mg ha−1). Combining our model with the climatic projections for Madagascar from 7 IPCC CMIP5 global climate models following the RCP 8.5, we forecast an average forest carbon stock loss of 17% (range: 7–24%) by the year 2080. For comparison, a spatially homogeneous deforestation of 0.5% per year on the same period would lead to a loss of 30% of the forest carbon stock. Synthesis. Our study shows that climate change is likely to induce a decrease in tropical forest carbon stocks. This loss could be due to a decrease in the average tree size and to shifts in tree species distribution, with the selection of small-statured species. In Madagascar, climate-induced carbon emissions might be, at least, of the same order of magnitude as emissions associated with anthropogenic deforestation.

Premise of the Study : Basic 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. Methods: Here, we derive a new exact formula to compute the basic wood density Db from the density at moisture content w denoted Dw, the fiber 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. Key Results: We 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 = 0.828D12. This value of 0.828 provides basic wood density estimates 4–5% smaller than values inferred from previous conversion factors. Conclusions: This new conversion factor should be used to derive basic wood densities in global wood density databases. Its use would prevent overestimating global forest carbon stocks and allow predicting better tree species community dynamics from wood density.

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.MethodsHere, 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.Key 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.ConclusionsThis 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.

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.

International audience; This study aimed to assess the potential of GLAS (Geoscience Laser Altimeter System) LiDAR data to overcome the saturation at high AGB values of existing AGB map on Madagascar (Vieilledent's AGB map [1]). First, spatially distributed estimations of AGB were obtained from GLAS data. Second, the difference between the Vieilledent's AGB map and GLAS derived AGB at each GLAS footprints location was calculated and a spatially distributed additional correction factors were obtained. Thanks to the spatial structure of these additional correction factors, an ordinary kriging interpolation was thus performed to provide a continuous correction factor map. Finally, the existing and the correction factor map were summed to improve the Vieilledent's AGB map. Results showed that the integration of GLAS data overcome the saturation at high AGB of Vieilledent's AGB map and allow AGB estimation until 650 t/ha (maximum AGB values from Vieilledent AGB map was 550 t/ha).

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.

Mapping forest AGB (Above Ground Biomass) is of crucial importance to estimate the carbon emissions associated with tropical deforestation. This study proposes a method to overcome the saturation at high AGB values of existing AGB map (Vieilledent’s AGB map) by using a map of correction factors generated from GLAS (Geoscience Laser Altimeter System) spaceborne LiDAR data. The Vieilledent’s AGB map of Madagascar was established using optical images, with parameters calculated from the SRTM Digital Elevation Model, climatic variables, and field inventories. In the present study, first, GLAS LiDAR data were used to obtain a spatially distributed (GLAS footprints geolocation) estimation of AGB (GLAS AGB) covering Madagascar forested areas, with a density of 0.52 footprint/km2. Second, the difference between the AGB from the Vieilledent’s AGB map and GLAS AGB at each GLAS footprint location was calculated, and additional spatially distributed correction factors were obtained. Third, an ordinary kriging interpolation was thus performed by taking into account the spatial structure of these additional correction factors to provide a continuous correction factor map. Finally, the existing and the correction factor maps were summed to improve the Vieilledent’s AGB map. The results showed that the integration of GLAS data improves the precision of Vieilledent’s AGB map by approximately 7 t/ha. By integrating GLAS data, the RMSE on AGB estimates decreases from 81 t/ha (R2 = 0.62) to 74.1 t/ha (R2 = 0.71). Most importantly, we showed that this approach using LiDAR data avoids underestimating high biomass values (new maximum AGB of 650 t/ha compared to 550 t/ha with the first approach).

Soil organic carbon (SOC) plays an important role in climate change regulation notably through release of CO2 following land use change such a deforestation, but data on stock change levels are lacking. This study aims to empirically assess SOC stocks change between 1991 and 2011 at the landscape scale using easy-to-access spatially-explicit environmental factors. The study area was located in southeast Madagascar, in a region that exhibits very high rate of deforestation and which is characterized by both humid and dry climates. We estimated SOC stock on 0.1 ha plots for 95 different locations in a 43,000 ha reference area covering both dry and humid conditions and representing different land cover including natural forest, cropland, pasture and fallows. We used the Random Forest algorithm to find out the environmental factors explaining the spatial distribution of SOC. We then predicted SOC stocks for two soil layers at 30 cm and 100 cm over a wider area of 395,000 ha. By changing the soil and vegetation indices derived from remote sensing images we were able to produce SOC maps for 1991 and 2011. Those estimates and their related uncertainties where combined in a post-processing step to map estimates of significant SOC variations and we finally compared the SOC change map with published deforestation maps. Results show that the geologic variables, precipitation, temperature, and soil-vegetation status were strong predictors of SOC distribution at regional scale. We estimated an average net loss of 10.7% and 5.2% for the 30 cm and the 100 cm layers respectively for deforested areas in the humid area. Our results also suggest that these losses occur within the first five years following deforestation. No significant variations were observed for the dry region. This study provides new solutions and knowledge for a better integration of soil threats and opportunities in land management policies.

While high resolution spatial variables contribute to a good fit of spatially-explicit deforestation models, socio-economic processes are often beyond the scope of these models. Such a low level of interest in the socio-economic dimension of deforestation limits the relevancy of these models for decision making and may be the cause of their failure to accurately predict observed deforestation trends in the medium term. This study aims to propose a flexible methodology for taking into account multiple drivers of deforestation in tropical forested areas, where the intensity of deforestation is explicitly predicted based on socio-economic variables. By coupling a model of deforestation location based on spatial environmental variables with several sub-models of deforestation intensity based on socio-economic variables, we were able to create a map of predicted deforestation over the period 2001–2014 in French Guiana. This map was compared to a reference map for accuracy assessment, not only at the pixel scale but also over cells ranging from 1 to approx. 600 sq. km. Highly significant relationships were explicitly established between deforestation intensity and several socio-economic variables: population growth, the amount of agricultural subsidies, gold and wood production. Such a precise characterisation of socio-economic processes allows to avoid overestimation biases in high deforestation areas, suggesting a better integration of socio-economic processes in the models. While considering deforestation as a purely geographical process contributes to the creation of conservative models unable to effectively assess changes in the socio-economic and political contexts influencing deforestation trends, this explicit characterisation of the socio-economic dimension of deforestation is critical for the creation of deforestation scenarios in REDD+ projects. This article is protected by copyright. All rights reserved.

Tropical forests of the Guiana Shield are the most affected by gold-mining in South America, experiencing an exponential increase in deforestation since the early 2000's. Using yearly deforestation data encompassing Guyana, Suriname, French Guiana and the Brazilian State of Amapa, we demonstrated a strong relationship between deforestation due to gold-mining and gold-prices at the regional scale. In order to assess additional drivers of deforestation due to gold-mining, we focused on the national scale and highlighted the heterogeneity of the response to gold-prices under different political contexts. Deforestation due to gold-mining over the Guiana Shield occurs mainly in Guyana and Suriname. On the contrary, past and current repressive policies in Amapa and French Guiana likely contribute to the decorrelation of deforestation and gold prices. In this work, we finally present a case study focusing on French Guiana and Suriname, two neighbouring countries with very different levels of law enforcement against illegal gold-mining. We developed a modelling framework to estimate potential deforestation leakages from French Guiana to Suriname in the border areas. Based on our assumptions, we estimated a decrease in deforestation due to gold-mining of approx. 4300 hectares in French Guiana and an increase of approx. 12 100 hectares in Suriname in response to the active military repression of illegal gold-mining launched in French Guiana. Gold-mining in the Guiana Shield provides challenging questions regarding REDD+ implementation. These questions are discussed at the end of this study and are important to policy makers who need to provide sustainable alternative employment to local populations in order to ensure the effectiveness of environmental policies. (Resume d'auteur)

The Grauer's gorilla is one of two subspecies of the Eastern gorilla. The whole population of Grauer's gorilla lives in the forests of eastern Democratic Republic of the Congo (DRC) in Central Africa. In 1996 a civil war started in the DRC, which killed 5 million people. We wanted to find out what effect the civil war had on the population of Grauer's gorillas. We carried out field surveys and used data from local communities and park rangers and found that the global population had decreased by 77% since 1994. There are now only 3,800 individuals left. This makes this ape Critically Endangered on the IUCN Red List of Threatened Species and now makes both gorilla species (Eastern and Western) and all four subspecies Critically Endangered. This is the highest category of threat before extinction.

GlobAllomeTree is an international platform for tree allometric equations. It is the first worldwide web platform designed to facilitate the access of the tree allometric equation and to facilitate the assessment of the tree biometric characteristics for commercial volume, bio-energy or carbon cycling. The webplatform presents a database containing tree allometric equations, a software called Fantallomatrik, to facilitate the comparison and selection of the equations, and documentation to facilitate the development of new tree allometric models, improve the evaluation of tree and forest resources and improve knowledge on tree allometric equations. In the Fantallometrik software, equations can be selected by country, ecological zones, input parameters, tree species, statistic parameters and outputs. The continuously updated database currently contains over 5000 tree allometric equations classified according to 73 fields. The software Fantallometrik can be also used to compare equations, insert new data and estimate the selected output variables using field inventory. The GlobAllomeTree products are freely available at the URL: http://globallometree.org for a range of users including foresters, project developers, scientist, student and government staff.

High resolution and low uncertainty deforestation maps covering large spatial areas in tropical countries are needed to plan efficient forest conservation and management programs such as REDD + (Reducing Emissions from Deforestation and Forest Degradation). Using an open-source free software (R, GRASS and QGis) and an original statistical approach combining multi-date land cover observations based on Landsat satellite images and the random forests classifier, we obtained up-to-date deforestation maps for the periods 2000–2005 and 2005–2010 with a minimum mapping unit of 0.36 ha for 7.7 M hectares, i.e. 40.3% of the tropical humid forest and 20.6% of the tropical dry forest in Madagascar. Uncertainty in deforestation on the maps was calculated by comparing the results of the classification to more than 30,000 visual interpretation points on a regular grid. We assessed accuracy on a per-pixel basis (confusion matrix) and by measuring the relative surface difference between wall-to-wall approach and point sampling. At the pixel level, user accuracy was 84.7% for stable land cover and 60.7% for land cover change. On average for the whole study area, we obtained a relative difference of 2% for stable land cover categories and 21.1% land cover change categories respectively between the wall-to-wall and the point sampling approach. Depending on the study area, our conservative assessment of annual deforestation rates ranged from 0.93 to 2.33%·yr− 1 for the humid forest and from 0.46 to 1.17%·yr− 1 for the dry forest. Here we describe an approach to obtain deforestation maps with reliable uncertainty estimates that can be transposed to other regions in the tropical world.

Anthropogenic deforestation in tropical countries is responsible for a significant part of global carbon dioxide emissions in the atmosphere. To plan efficient climate change mitigation programs (such as REDD+, Reducing Emissions from Deforestation and forest Degradation), reliable forecasts of deforestation and carbon dioxide emissions are necessary. Although population density has been recognized as a key factor in tropical deforestation, current methods of prediction do not allow the population explosion that is occurring in many tropical developing countries to be taken into account. Here, we propose an innovative approach using novel computational and statistical tools, including R/GRASS scripts and the new phcfM R package, to model the intensity and location of deforestation including the effect of population density. We used the model to forecast anthropogenic deforestation and carbon dioxide emissions in five large study areas in the humid and spiny-dry forests of Madagascar. Using our approach, we were able to demonstrate that the current rapid population growth in Madagascar (+3.39% per year) will significantly increase the intensity of deforestation by 2030 (up to +1.17% per year in densely populated areas). We estimated the carbon dioxide emissions associated with the loss of aboveground biomass to be of 2.24 and 0.26 tons per hectare and per year in the humid and spiny-dry forest, respectively. Our models showed better predictive ability than previous deforestation models (the figure of merit ranged from 10 to 23). We recommend this approach to reduce the uncertainty associated with deforestation forecasts. We also underline the risk of an increase in the speed of deforestation in the short term in tropical developing countries undergoing rapid population expansion.

International audience; Phenotypic traits and their associated trade-offs have been shown to have globally consistent effects on individual plant physiological functions1, 2, 3, but how these effects scale up to influence competition, a key driver of community assembly in terrestrial vegetation, has remained unclear4. Here we use growth data from more than 3 million trees in over 140,000 plots across the world to show how three key functional traits-wood density, specific leaf area and maximum height-consistently influence competitive interactions. Fast maximum growth of a species was correlated negatively with its wood density in all biomes, and positively with its specific leaf area in most biomes. Low wood density was also correlated with a low ability to tolerate competition and a low competitive effect on neighbours, while high specific leaf area was correlated with a low competitive effect. Thus, traits generate trade-offs between performance with competition versus performance without competition, a fundamental ingredient in the classical hypothesis that the coexistence of plant species is enabled via differentiation in their successional strategies5. Competition within species was stronger than between species, but an increase in trait dissimilarity between species had little influence in weakening competition. No benefit of dissimilarity was detected for specific leaf area or wood density, and only a weak benefit for maximum height. Our trait-based approach to modelling competition makes generalization possible across the forest ecosystems of the world and their highly diverse species composition.