First mission - towards a global harmonised in-situ data repository for forest biomass datasets validation

Global measurements of forest height, biomass are urgently needed as essential climate and ecosystem variables, but can benefit from greater co-operation between remote sensing (RS) and forest ecological communities. The Forest Observation System - FOS (https://forest-observation-system.net/ [https://forest-observation- system.net/]) is an international cooperation to establish a global in-situ forest biomass database to support earth observation and to encourage investment in relevant field-based observations and science. FOS aims to link the RS community with ecologists who measure forest biomass and estimating biodiversity in the field. The FOS aims to overcome data sharing issues and introduce a standard biomass data flow from tree-level measurement to the plot-level aggregation served in the most suitable form for the RS. Ecologists benefit from the FOS with improved access to global biomass information, data standards, gap identification and potentially improved funding opportunities to address the known gaps and deficiencies in the data. FOS closely collaborate with the CTFS-ForestGEO, the ForestPlots.net (incl. RAfNFOR, AfriTRON and T-FORCES), AusCover, TmFO and the llASA network. FOS is an open initiative with other networks and teams most welcome to join. The online database provides open access for forest plot location, canopy height and above-ground biomass. Plot size is 0.25ha or larger. Comparison of plot biomass data with available global and regional maps (incl. Kindermann et al., 2013; Thurner et al., 2013; Saatchi et al., 2011; Baccini et al., 2012; Avitabile et al., 2016; Hu et al., 2016; Santoro et al., 2018) shows wide range of uncertainties associated with biomass estimation

Modeling forest biomass of the Congo basin from extensive commercial inventories

Mapping the vegetation Carbon stocks is crucial to understand the global climate change. The Carbon stock maps have direct implications in economy and environmental policy. This is especially true in tropical forests where most of the uncertainties on carbon fluxes and stocks are concentrated. Substantial efforts have been done recently to map forest carbon in tropical areas, especially by using remote sensing-based approaches. However, there is no way to bypass a calibration step where biomass is locally measured through forest inventories. The great importance of this learning step and its possible issues has been documented, highlighting the importance of terrestrial datasets. In our work, we have gathered a very large dataset of forest inventories covering the Congo Basin. It consists of 73 000 0.5ha plots of commercial inventories covering 4 million hectares in Cameroon, Republic of Congo, Gabon, Central African Republic, and the Democratic Republic of the Congo. These terrestrial data are of great value to understand and model the spatial distribution of various forest properties, among which the Carbon stock. They can also make a great tool to control and improve the performance of the remote sensing methods. In our study, we rely on these plots to test the validity of previously published pantropical Carbon maps. After gathering the data with extra care due to the heterogeneous inventory methods, we used bioclimatic models, topography, and remote sensing observation to extrapolate the forest carbon estimates at the Congo basin scale. (Texte intégral

Altitudinal gradients of tree species diversity and above-ground biomass on a small montane of Atlantic Central Africa

Tropical forests are both important carbon sinks and among the most biodiverse ecosystems on the earth. Patterns in aboveground biomass (AGB) and their relationship with species diversity of tropical forests over short altitudinal gradients are poorly known and the few previous studies on the subject have yielded variable results. Here, focusing on old-growth forests in Atlantic central Africa, we investigated how AGB varies with altitude, and how this variation is related to altitudinal changes in floristic composition and/or forest structure. We also investigate the relationship between AGB and species diversity along the altitudinal gradient. We inventoried all trees with a diameter (dbh) ≥ 10 cm in fifteen 1 ha permanent plots (100 m x 100 m) established along a transect from lowland (200 m) to submontane forests (900 m) in the Ngovayang Massif, southwestern Cameroon. Our data show a negative relationship between AGB and tree species richness, related to the elevation gradient. Forest AGB varied two-fold along this gradient, decreasing from 500-600 Mg ha-1 in lowland plots to less than 300 Mg ha-1 at the highest altitudes, while diversity increased, from 35.4 to 54.6 (Fisher's alpha index). The decreasing trend in AGB was mainly due to large trees (dbh ≥ 70 cm) whose contribution to AGB significantly decreased with altitude while the contribution from smaller trees was constant. Tree height and basal area also decreased significantly with increasing altitude, whereas stem density increased. While maximum potential tree height significantly decreased, wood specific gravity displayed no trend along the gradient. In particular, we showed that AGB variation was mainly determined by shift in species composition because large tree species were filtered out in the highest altitudes. Hence, our work further highlight the need for studying the drivers of large tree species distribution to better understand forest carbon stock variations in tropical forests. At the regional level, the Ngovayang massif was among the richest sites with highest level of biomass. Our results have strong implications in decisions on balancing carbon sequestration strategies with biodiversity conservation ones. Policy consequences are particularly relevant in forest management and land use planning.(Texte intégral

Functional shifts within Central African rainforests

Background: Understanding the reaction of ecosystems to climate change and anthropogenic pressure is a central question in ecology and environmental sciences. In the terrestrial tropics, theoretical and empirical works suggest that once external disturbances have reached a given threshold, forest-savanna systems can switch from one state to another. Considering the multiplicity of the tropical forest systems, we make the assumption that numerous shifts may actually occur within the forest itself, without changes in forest cover but with risks of critical modifications in forest functioning. Methods: To test this hypothesis, we used a finite mixture of regression models aiming at simultaneously predicting and grouping forest functional profiles at the stand level with respect to anthropogenic pressure, climate and soil. The model is built on a dataset of more than 140 000 plots of 0.5-ha each gathered from Central African forest companies. Forest stand functions are analyzed through two key functional traits: the successional status - pioneer vs. non-pioneer trees- and the leaf phenology - evergreen vs. deciduous trees. Results: Our model captured a significant part of variation in the functional composition over the study area and revealed how anthropogenic pressure, climate change, soils or their combination lead to profound modifications within the forests. In particular, we showed that shifts from evergreen to deciduous stands can be mediated both by anthropogenic pressure or climate change. Discussion: This work shows for the first time how external forcing may jointly lead to multiple shifts in the functional composition of tropical forests. Our model allowed to predict directional changes in forest functioning according to anthropogenic pressure and climate thus opening new perspectives in theoretical ecology, global vegetation modelling and in the understanding of the vulnerability of tropical forests to global changes. (Texte intégral

De l'arbre au satellite : comment cartographier la diversité des forêts tropicales d'Afrique Centrale ?

Les forêts d'Afrique centrale vont subir de profondes modifications liées au changement climatique ces prochaines décennies. Pour que les sociétés humaines puissent anticiper ou s'adapter à ces changements, il est nécessaire de bien comprendre les mécanismes de fonctionnement de ces forêts et d'être à même de spatialiser leur variabilité. A partir d'une série temporelle de quatorze années d'images satellites MODIS à 250 m de résolution par pixel, nous présentons ici une nouvelle carte des forêts d'Afrique centrale. La production de cette carte, basée sur le comportement phénologique de la canopée, reflète l'activité de la végétation en fonction des saisons. (Résumé d'auteur

Functional shifts within central African rainforests

Understanding the reaction of ecosystems to climate change and anthropogenic pressure is a central question in ecology and environmental sciences. In the terrestrial tropics, theoretical and empirical works suggest that once external disturbances have reached a given threshold, forest-savanna systems can switch from one state to another. Considering the multiplicity of the tropical forest systems, we make the assumption that numerous shifts may actually occur within the forest itself, without changes in forest cover but with risks of critical modifications in forest functioning. To test this hypothesis, we used a finite mixture of regression models aiming at simultaneously predicting and grouping forest functional profiles at the stand level with respect to anthropogenic pressure, climate and soil. The model is built on a dataset of more than 140 000 plots of 0.5-ha each gathered from Central African forest companies. Forest stand functions are analyzed through two key functional traits: the successional status - pioneer vs. non-pioneer trees- and the leaf phenology - evergreen vs. deciduous trees. Our model captured a significant part of variation in the functional composition over the study area and revealed how anthropogenic pressure, climate change, soils or their combination lead to profound modifications within the forests. In particular, we showed that shifts from evergreen to deciduous stands can be mediated both by anthropogenic pressure or climate change. This work shows for the first time how external forcing may jointly lead to multiple shifts in the functional composition of tropical forests. Our model allowed to predict directional changes in forest functioning according to anthropogenic pressure and climate thus opening new perspectives in theoretical ecology, global vegetation modelling and in the understanding of the vulnerability of tropical forests to global changes

Predicting forest composition across space and time in central African forests

Predicting the current and future natural distributions of species is challenging, especially in the tropics where large remote areas remain poorly known. Such challenge can only be met with an in-depth understanding of the drivers of species distribution, a well-designed and extensive survey and appropriate statistical models. In this study, we use a large dataset of forest inventories from logging companies, which provides information on the abundance of 215 tree genera, in more than 115,000 plots spread over four Central African countries. In order to predict the current and future distribution of these tree genera, we use a set of bioclimatic, geological and anthropogenic variables. We rely on a recently published methodology, called Supervised Component Generalized Linear Regression (SCGLR), which identifies the most predictive dimensions among a large set of predictors. Using a calibration and validation scheme, we show that the distribution of most tree genera can be well predicted over the whole study area. At the community level, the floristic and functional composition of tree genera is inferred with a high accuracy. Finally, using climatic and anthropogenic scenarios we predict the expected change in functional and phylogenetic structure of tree communities over the western part of the Congo Basin. Overall, our study provides useful ecological insights and shows that tropical tree distributions can be predicted with good accuracy, offering new perspectives to manage tropical forests at large spatial scales. (Texte intégral

Can we predict forest composition across space and time in Central Africa

Background. Predicting the current and future natural distributions of species is challenging, especially in the tropics where large remote areas remain poorly known. Such challenge can only be met with an in-depth understanding of the drivers of species distribution, a well-designed and extensive survey and appropriate statistical models. Method. In this study, we use a large dataset of forest inventories from logging companies, which provides information on the abundance of 123 tree genera, in 140,000 plots spread over four Central African countries. In order to predict the current and future distribution of these tree genera, we use a set of bioclimatic, geological and anthropogenic variables. We rely on a recently published methodology, called Supervised Component Generalized Linear Regression (SCGLR), which identifies the most predictive dimensions among a large set of predictors. Result. Using a calibration and validation scheme, we show that the distribution of most tree genera can be well predicted over the whole study area at the present time. At the community level, the floristic and functional composition of tree genera is also inferred with a good accuracy. Finally, using spatially explicit null models, we show that species-climate association are in most cases not better than chance, thus challenging our ability to predict how forest composition will be affected by climatic changes. Conclusion. Overall, our study shows that tropical tree distributions can be predicted with good accuracy at the present time, offering new perspectives to manage tropical forests at large spatial scales, but that predicting shifts in species distribution under climate change scenarios is challenging. (Texte intégral

Gateway to the forests of Central Africa: towards a unified collaborative model of forest dynamics

Background: The tropical forests in general, and those of Central Africa in particular, stand at the cross-road. The combined and interacting effects of land-use change, resource extraction, defaunation, fire, fragmentation and climate change are pushing these ecosystems towards critical points where transitions to altered states will happen. The future of these forests depends on our capacity to understand and anticipate these transitions, and to identify these states the forest ecosystems are likely to take. Yet, to date, there was not a unified model that presented the best available knowledge on the forest dynamics of the regions. The field is highly fragmented and we lack a general overview. Method: We propose here a general unified model of forest dynamics in Central Africa. This model represents the best available knowledge on the topic and is the result of a collaborative effort based on expert knowledge and an analysis of the literature. We built it using methods issued from the fields of facilitation, participatory modelling and team science. Result: Our model identifies the main forest types present in the area, the dynamic links and possible transitions between them and the potential impacts of environmental factors - climate, soil, large mammals - and human factors - logging, fire, clearings. It provides a description of these forest types and allows the layman to grasp the general dynamics at play in the region. For those willing to deepen their understanding, we provide all the necessary literature leads to guide them in their discovery of the topic. Conclusion: Our aim is to propose as an easy to access gateway for those needing to take decisions on how to manage the forests of Central Africa in the coming decades. It sums up our current understanding of the system, helps chart knowledge gaps and highlight avenues for future research, serving as basis for discussions. An accepted common understanding of the dynamics of these forests will be solid foundation for alternative modes of management to emerge, we hope it will foster dialog between key stakeholders, and generate better informed decisions, more resilient to surprises. (Texte intégral