Unveiling African rainforest composition and vulnerability to global change.

Africa is forecasted to experience large and rapid climate change1 and population growth2 during the twenty-first century, which threatens the world's second largest rainforest. Protecting and sustainably managing these African forests requires an increased understanding of their compositional heterogeneity, the environmental drivers of forest composition and their vulnerability to ongoing changes. Here, using a very large dataset of 6 million trees in more than 180,000 field plots, we jointly model the distribution in abundance of the most dominant tree taxa in central Africa, and produce continuous maps of the floristic and functional composition of central African forests. Our results show that the uncertainty in taxon-specific distributions averages out at the community level, and reveal highly deterministic assemblages. We uncover contrasting floristic and functional compositions across climates, soil types and anthropogenic gradients, with functional convergence among types of forest that are floristically dissimilar. Combining these spatial predictions with scenarios of climatic and anthropogenic global change suggests a high vulnerability of the northern and southern forest margins, the Atlantic forests and most forests in the Democratic Republic of the Congo, where both climate and anthropogenic threats are expected to increase sharply by 2085. These results constitute key quantitative benchmarks for scientists and policymakers to shape transnational conservation and management strategies that aim to provide a sustainable future for central African forests.

40 years of forest dynamics and tree demography in an intact tropical forest at M'Baïki in central Africa.

A vast silvicultural experiment was set up in 1982 nearby the town of M'Baïki in the Central African Republic to monitor the recovery of tropical forests after disturbance. The M'Baïki experiment consists of ten 4-ha Permanent Sample Plots (PSPs) that were assigned to three silvicultural treatments in 1986 according to a random block design. In each plot, all trees with a girth at breast height greater than 30 cm were spatially located, numbered, measured, and determined botanically. Girth, mortality and newly recruited trees, were monitored almost annually over the 1982-2022 period with inventory campaigns for 35 years. The data were earlier used to fit growth and population models, to study the species composition dynamics, and the effect of silvicultural treatments on tree diversity and aboveground biomass. Here, we present new information on the forest stand structure dynamics and tree demography. The data released from this paper cover the three control plots and constitute a major contribution for further studies about the biodiversity of intact tropical forests.

Phylostems: a new graphical tool to investigate temporal signal of heterochronous sequences datasets.

Motivation: Molecular tip-dating of phylogenetic trees is a growing discipline that uses DNA sequences sampled at different points in time to co-estimate the timing of evolutionary events with rates of molecular evolution. Importantly, such inferences should only be performed on datasets displaying sufficient temporal signal, a feature important to test prior to any tip-dating inference. For this purpose, the most popular method considered to-date has been the 'root-to-tip regression' which consist in fitting a linear regression of the number of substitutions accumulated from the root to the tips of a phylogenetic tree as a function of sampling times. The main limitation of the regression method, in its current implementation, relies in the fact that the temporal signal can only be tested at the whole-tree scale (i.e. its root). Results: To overcome this limitation we introduce Phylostems, a new graphical user-friendly tool developed to investigate temporal signal within every clade of a phylogenetic tree. We provide a 'how to' guide by running Phylostems on an empirical dataset and supply guidance for results interpretation. Availability and implementation: Phylostems is freely available at https://pvbmt-apps.cirad.fr/apps/phylostems.

Spatial validation reveals poor predictive performance of large-scale ecological mapping models.

Mapping aboveground forest biomass is central for assessing the global carbon balance. However, current large-scale maps show strong disparities, despite good validation statistics of their underlying models. Here, we attribute this contradiction to a flaw in the validation methods, which ignore spatial autocorrelation (SAC) in data, leading to overoptimistic assessment of model predictive power. To illustrate this issue, we reproduce the approach of large-scale mapping studies using a massive forest inventory dataset of 11.8 million trees in central Africa to train and validate a random forest model based on multispectral and environmental variables. A standard nonspatial validation method suggests that the model predicts more than half of the forest biomass variation, while spatial validation methods accounting for SAC reveal quasi-null predictive power. This study underscores how a common practice in big data mapping studies shows an apparent high predictive power, even when predictors have poor relationships with the ecological variable of interest, thus possibly leading to erroneous maps and interpretations.

A map of African humid tropical forest aboveground biomass derived from management inventories.

Forest biomass is key in Earth carbon cycle and climate system, and thus under intense scrutiny in the context of international climate change mitigation initiatives (e.g. REDD+). In tropical forests, the spatial distribution of aboveground biomass (AGB) remains, however, highly uncertain. There is increasing recognition that progress is strongly limited by the lack of field observations over large and remote areas. Here, we introduce the Congo basin Forests AGB (CoFor-AGB) dataset that contains AGB estimations and associated uncertainty for 59,857 1-km pixels aggregated from nearly 100,000 ha of in situ forest management inventories for the 2000 - early 2010s period in five central African countries. A comprehensive error propagation scheme suggests that the uncertainty on AGB estimations derived from c. 0.5-ha inventory plots (8.6-15.0%) is only moderately higher than the error obtained from scientific sampling plots (8.3%). CoFor-AGB provides the first large scale view of forest AGB spatial variation from field data in central Africa, the second largest continuous tropical forest domain of the world.

Temperature rising would slow down tropical forest dynamic in the Guiana Shield.

Increasing evidence shows that the functioning of the tropical forest biome is intimately related to the climate variability with some variables such as annual precipitation, temperature or seasonal water stress identified as key drivers of ecosystem dynamics. How tropical tree communities will respond to the future climate change is hard to predict primarily because several demographic processes act together to shape the forest ecosystem general behavior. To overcome this limitation, we used a joint individual-based model to simulate, over the next century, a tropical forest community experiencing the climate change expected in the Guiana Shield. The model is climate dependent: temperature, precipitation and water stress are used as predictors of the joint growth and mortality rates. We ran simulations for the next century using predictions of the IPCC 5AR, building three different climate scenarios (optimistic RCP2.6, intermediate, pessimistic RCP8.5) and a control (current climate). The basal area, above-ground fresh biomass, quadratic diameter, tree growth and mortality rates were then computed as summary statistics to characterize the resulting forest ecosystem. Whatever the scenario, all ecosystem process and structure variables exhibited decreasing values as compared to the control. A sensitivity analysis identified the temperature as the strongest climate driver of this behavior, highlighting a possible temperature-driven drop of 40% in average forest growth. This conclusion is alarming, as temperature rises have been consensually predicted by all climate scenarios of the IPCC 5AR. Our study highlights the potential slow-down danger that tropical forests will face in the Guiana Shield during the next century.

An evolutionary perspective on leaf economics: phylogenetics of leaf mass per area in vascular plants.

In plant leaves, resource use follows a trade-off between rapid resource capture and conservative storage. This "worldwide leaf economics spectrum" consists of a suite of intercorrelated leaf traits, among which leaf mass per area, LMA, is one of the most fundamental as it indicates the cost of leaf construction and light-interception borne by plants. We conducted a broad-scale analysis of the evolutionary history of LMA across a large dataset of 5401 vascular plant species. The phylogenetic signal in LMA displayed low but significant conservatism, that is, leaf economics tended to be more similar among close relatives than expected by chance alone. Models of trait evolution indicated that LMA evolved under weak stabilizing selection. Moreover, results suggest that different optimal phenotypes evolved among large clades within which extremes tended to be selected against. Conservatism in LMA was strongly related to growth form, as were selection intensity and phenotypic evolutionary rates: woody plants showed higher conservatism in relation to stronger stabilizing selection and lower evolutionary rates compared to herbaceous taxa. The evolutionary history of LMA thus paints different evolutionary trajectories of vascular plant species across clades, revealing the coordination of leaf trait evolution with growth forms in response to varying selection regimes.

Vegetation structure and greenness in Central Africa from Modis multi-temporal data.

African forests within the Congo Basin are generally mapped at a regional scale as broad-leaved evergreen forests, with the main distinction being between terra-firme and swamp forest types. At the same time, commercial forest inventories, as well as national maps, have highlighted a strong spatial heterogeneity of forest types. A detailed vegetation map generated using consistent methods is needed to inform decision makers about spatial forest organization and their relationships with environmental drivers in the context of global change. We propose a multi-temporal remotely sensed data approach to characterize vegetation types using vegetation index annual profiles. The classifications identified 22 vegetation types (six savannas, two swamp forests, 14 forest types) improving existing vegetation maps. Among forest types, we showed strong variations in stand structure and deciduousness, identifying (i) two blocks of dense evergreen forests located in the western part of the study area and in the central part on sandy soils; (ii) semi-deciduous forests are located in the Sangha River interval which has experienced past fragmentation and human activities. For all vegetation types enhanced vegetation index profiles were highly seasonal and strongly correlated to rainfall and to a lesser extent, to light regimes. These results are of importance to predict spatial variations of carbon stocks and fluxes, because evergreen/deciduous forests (i) have contrasted annual dynamics of photosynthetic activity and foliar water content and (ii) differ in community dynamics and ecosystem processes.

Geological substrates shape tree species and trait distributions in African moist forests.

BACKGROUND: Understanding the factors that shape the distribution of tropical tree species at large scales is a central issue in ecology, conservation and forest management. The aims of this study were to (i) assess the importance of environmental factors relative to historical factors for tree species distributions in the semi-evergreen forests of the northern Congo basin; and to (ii) identify potential mechanisms explaining distribution patterns through a trait-based approach. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed the distribution patterns of 31 common tree species in an area of more than 700,000 km(2) spanning the borders of Cameroon, the Central African Republic, and the Republic of Congo using forest inventory data from 56,445 0.5-ha plots. Spatial variation of environmental (climate, topography and geology) and historical factors (human disturbance) were quantified from maps and satellite records. Four key functional traits (leaf phenology, shade tolerance, wood density, and maximum growth rate) were extracted from the literature. The geological substrate was of major importance for the distribution of the focal species, while climate and past human disturbances had a significant but lesser impact. Species distribution patterns were significantly related to functional traits. Species associated with sandy soils typical of sandstone and alluvium were characterized by slow growth rates, shade tolerance, evergreen leaves, and high wood density, traits allowing persistence on resource-poor soils. In contrast, fast-growing pioneer species rarely occurred on sandy soils, except for Lophira alata. CONCLUSIONS/SIGNIFICANCE: The results indicate strong environmental filtering due to differential soil resource availability across geological substrates. Additionally, long-term human disturbances in resource-rich areas may have accentuated the observed patterns of species and trait distributions. Trait differences across geological substrates imply pronounced differences in population and ecosystem processes, and call for different conservation and management strategies.