Aim Large trees (d.b.h.≥70 cm) store large amounts of biomass. Several studies suggest that large trees may be vulnerable to changing climate, potentially leading to declining forest biomass storage. Here we determine the importance of large trees for tropical forest biomass storage and explore which intrinsic (species trait) and extrinsic (environment) variables are associated with the density of large trees and forest biomass at continental and pan-tropical scales. Location Pan-tropical. Methods Aboveground biomass (AGB) was calculated for 120 intact lowland moist forest locations. Linear regression was used to calculate variation in AGB explained by the density of large trees. Akaike information criterion weights (AICc-wi) were used to calculate averaged correlation coefficients for all possible multiple regression models between AGB/density of large trees and environmental and species trait variables correcting for spatial autocorrelation. Results Density of large trees explained c. 70% of the variation in pan-tropical AGB and was also responsible for significantly lower AGB in Neotropical [287.8 (mean)±105.0 (SD) Mg ha -1 versus Palaeotropical forests (Africa 418.3±91.8 Mg ha-1; Asia 393.3±109.3 Mg ha-1). Pan-tropical variation in density of large trees and AGB was associated with soil coarseness (negative), soil fertility (positive), community wood density (positive) and dominance of wind dispersed species (positive), temperature in the coldest month (negative), temperature in the warmest month (negative) and rainfall in the wettest month (positive), but results were not always consistent among continents. Main conclusions Density of large trees and AGB were significantly associated with climatic variables, indicating that climate change will affect tropical forest biomass storage. Species trait composition will interact with these future biomass changes as they are also affected by a warmer climate. Given the importance of large trees for variation in AGB across the tropics, and their sensitivity to climate change, we emphasize the need for in-depth analyses of the community dynamics of large trees. (Résumé d'auteur

The high species richness of tropical forests has long been recognized, yet there remains substantial uncertainty regarding the actual number of tropical tree species. Using a pantropical tree inventory database from closed canopy forests, consisting of 657,630 trees belonging to 11,371 species, we use a fitted value of Fisher's alpha and an approximate pantropical stem total to estimate the minimum number of tropical forest tree species to fall between ∼ 40,000 and ∼ 53,000, i.e., at the high end of previous estimates. Contrary to common assumption, the Indo-Pacific region was found to be as species-rich as the Neotropics, with both regions having a minimum of ∼ 19,000-25,000 tree species. Continental Africa is relatively depauperate with a minimum of ∼ 4,500-6,000 tree species. Very few species are shared among the African, American, and the Indo-Pacific regions. We provide a methodological framework for estimating species richness in trees that may help refine species richness estimates of tree-dependent taxa.

Tropical Africa is home to an astonishing biodiversity occurring in a variety of ecosystems. Past climatic change and geological events have impacted the evolution and diversification of this biodiversity. During the last two decades, around 90 dated molecular phylogenies of different clades across animals and plants have been published leading to an increased understanding of the diversification and speciation processes generating tropical African biodiversity. In parallel, extended geological and palaeoclimatic records together with detailed numerical simulations have refined our understanding of past geological and climatic changes in Africa. To date, these important advances have not been reviewed within a common framework. Here, we critically review and synthesize African climate, tectonics and terrestrial biodiversity evolution throughout the Cenozoic to the mid‐Pleistocene, drawing on recent advances in Earth and life sciences. We first review six major geo‐climatic periods defining tropical African biodiversity diversification by synthesizing 89 dated molecular phylogeny studies. Two major geo‐climatic factors impacting the diversification of the sub‐Saharan biota are highlighted. First, Africa underwent numerous climatic fluctuations at ancient and more recent timescales, with tectonic, greenhouse gas, and orbital forcing stimulating diversification. Second, increased aridification since the Late Eocene led to important extinction events, but also provided unique diversification opportunities shaping the current tropical African biodiversity landscape. We then review diversification studies of tropical terrestrial animal and plant clades and discuss three major models of speciation: (i) geographic speciation via vicariance (allopatry); (ii) ecological speciation impacted by climate and geological changes, and (iii) genomic speciation via genome duplication. Geographic speciation has been the most widely documented to date and is a common speciation model across tropical Africa. We conclude with four important challenges faced by tropical African biodiversity research: (i) to increase knowledge by gathering basic and fundamental biodiversity information; (ii) to improve modelling of African geophysical evolution throughout the Cenozoic via better constraints and downscaling approaches; (iii) to increase the precision of phylogenetic reconstruction and molecular dating of tropical African clades by using next generation sequencing approaches together with better fossil calibrations; (iv) finally, as done here, to integrate data better from Earth and life sciences by focusing on the interdisciplinary study of the evolution of tropical African biodiversity in a wider geodiversity context.

Large tropical trees and a few dominant species were recently identified as the main structuring elements of tropical forests. However, such result did not translate yet into quantitative approaches which are essential to understand, predict and monitor forest functions and composition over large, often poorly accessible territories. Here we show that the above-ground biomass (AGB) of the whole forest can be predicted from a few large trees and that the relationship is proved strikingly stable in 175 1-ha plots investigated across 8 sites spanning Central Africa. We designed a generic model predicting AGB with an error of 14% when based on only 5% of the stems, which points to universality in forest structural properties. For the first time in Africa, we identified some dominant species that disproportionally contribute to forest AGB with 1.5% of recorded species accounting for over 50% of the stock of AGB. Consequently, focusing on large trees and dominant species provides precise information on the whole forest stand. This offers new perspectives for understanding the functioning of tropical forests and opens new doors for the development of innovative monitoring strategies.

The Red List Categories and the accompanying five criteria developed by the International Union for Conservation of Nature (IUCN) provide an authoritative and comprehensive methodology to assess the conservation status of organisms. Red List criterion B, which principally uses distribution data, is the most widely used to assess conservation status, particularly of plant species. No software package has previously been available to perform large‐scale multispecies calculations of the three main criterion B parameters [extent of occurrence (EOO), area of occupancy (AOO) and an estimate of the number of locations] and provide preliminary conservation assessments using an automated batch process. We developed ConR, a dedicated R package, as a rapid and efficient tool to conduct large numbers of preliminary assessments, thereby facilitating complete Red List assessment. ConR (1) calculates key geographic range parameters (AOO and EOO) and estimates the number of locations sensu IUCN needed for an assessment under criterion B; (2) uses this information in a batch process to generate preliminary assessments of multiple species; (3) summarize the parameters and preliminary assessments in a spreadsheet; and (4) provides a visualization of the results by generating maps suitable for the submission of full assessments to the IUCN Red List. ConR can be used for any living organism for which reliable georeferenced distribution data are available. As distributional data for taxa become increasingly available via large open access datasets, ConR provides a novel, timely tool to guide and accelerate the work of the conservation and taxonomic communities by enabling practitioners to conduct preliminary assessments simultaneously for hundreds or even thousands of species in an efficient and time‐saving way.

BackgroundUnderstanding the patterns of biodiversity distribution and what influences them is a fundamental pre-requisite for effective conservation and sustainable utilisation of biodiversity. Such knowledge is increasingly urgent as biodiversity responds to the ongoing effects of global climate change. Nowhere is this more acute than in species-rich tropical Africa, where so little is known about plant diversity and its distribution. In this paper, we use RAINBIO – one of the largest mega-databases of tropical African vascular plant species distributions ever compiled – to address questions about plant and growth form diversity across tropical Africa.ResultsThe filtered RAINBIO dataset contains 609,776 georeferenced records representing 22,577 species. Growth form data are recorded for 97% of all species. Records are well distributed, but heterogeneous across the continent. Overall, tropical Africa remains poorly sampled. When using sampling units (SU) of 0.5°, just 21 reach appropriate collection density and sampling completeness, and the average number of records per species per SU is only 1.84. Species richness (observed and estimated) and endemism figures per country are provided. Benin, Cameroon, Gabon, Ivory Coast and Liberia appear as the botanically best-explored countries, but none are optimally explored. Forests in the region contain 15,387 vascular plant species, of which 3013 are trees, representing 5–7% of the estimated world’s tropical tree flora. The central African forests have the highest endemism rate across Africa, with approximately 30% of species being endemic.ConclusionsThe botanical exploration of tropical Africa is far from complete, underlining the need for intensified inventories and digitization. We propose priority target areas for future sampling efforts, mainly focused on Tanzania, Atlantic Central Africa and West Africa. The observed number of tree species for African forests is smaller than those estimated from global tree data, suggesting that a significant number of species are yet to be discovered. Our data provide a solid basis for a more sustainable management and improved conservation of tropical Africa’s unique flora, and is important for achieving Objective 1 of the Global Strategy for Plant Conservation 2011–2020. In turn, RAINBIO provides a solid basis for a more sustainable management and improved conservation of tropical Africa’s unique flora.Electronic supplementary materialThe online version of this article (doi:10.1186/s12915-017-0356-8) contains supplementary material, which is available to authorized users.

Aim Species distribution modelling typically relies completely or partially on climatic variables as predictors, overlooking the fact that these are themselves predictions with associated uncertainties. This is particularly critical when such predictors are interpolated between sparse station data, such as in the tropics. The goal of this study is to provide a new set of satellite‐based climatic predictor data and to evaluate its potential to improve modelled species–climate associations and transferability to novel geographical regions. Location Rain forests areas of Central Africa, the Western Ghats of India and South America. Methods We compared models calibrated on the widely used WorldClim station‐interpolated climatic data with models where either temperature or precipitation data from WorldClim were replaced by data from CRU, MODIS, TRMM and CHIRPS. Each predictor set was used to model 451 plant species distributions. To test for chance associations, we devised a null model with which to compare the accuracy metric obtained for every species. Results Fewer than half of the studied rain forest species distributions matched the climatic pattern better than did random distributions. The inclusion of MODIS temperature and CHIRPS precipitation estimates derived from remote sensing each allowed for a better than random fit for respectively 40% and 22% more species than models calibrated on WorldClim. Furthermore, their inclusion was positively related to a better transferability of models to novel regions. Main conclusions We provide a newly assembled dataset of ecologically meaningful variables derived from MODIS and CHIRPS for download, and provide a basis for choosing among the plethora of available climate datasets. We emphasize the need to consider the method used in the production of climate data when working on a region with sparse meteorological station data. In this context, remote sensing data should be the preferred choice, particularly when model transferability to novel climates or inferences on causality are invoked.

Aim To delineate bioregions in tropical Africa and determine whether different plant growth forms (trees, terrestrial herbs, lianas and shrubs) display the same pattern of regionalization, diversity and endemism as the whole flora. Location Tropical Africa (excl. Madagascar), from 20° N to 25° S. Taxon Vascular plants. Methods Analyses were based on occurrences of 24,719 vascular plant species distributed across tropical Africa extracted from the RAINBIO database. The majority of species (93%) were classified into four growth forms: terrestrial herbs, trees, shrubs and lianas. Biogeographical regions (bioregions) were delimited using a bipartite network clustering approach on the whole dataset and then separately for each growth form. Relationships among bioregions were investigated using non‐metric multidimensional scaling ordination, flora nestedness and endemism patterns. Results Analyses of the whole dataset identified 16 bioregions and 11 transition zones. These were congruent with most of the currently recognized phytogeographical classifications, and also highlighted previously under‐recognized bioregions. Bioregion endemism rates were lower and species richness higher when compared to estimates from the White/Association pour l'Etude Taxonomique de la Flore d'Afrique Tropicale (AETFAT) classification. Analysed separately, plant growth forms showed contrasting geographical patterns. Bioregionalization was better resolved for closed forest types using trees and lianas and for open vegetation types using terrestrial herbs, while shrubs showed good discriminative power in all vegetation types. Main conclusions We show that distribution patterns based on solely trees are not sufficient to define floristic bioregions in tropical Africa. Analyses of spatial patterns using different growth forms are complementary, likely reflecting different evolutionary processes and ecological relationships. The contribution of growth forms to delimit geographical floristic patterns across tropical Africa is of critical importance for land use planning and management, and for selecting priority conservation areas.