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Curbing biodiversity loss and its impact on ecosystem services, resilience and Nature’s Contributions to People is one of the main challenges of our generation (IPBES, 2019b, 2019a; Secretariat of the United Nations Convention on Biological Diversity, 2020). A global baseline assessment of the threat status of all of biodiversity is crucial to monitor the progress of conservation policies worldwide (Mace & al., 2000; Secretariat of the United Nations Convention on Biological Diversity, 2021) and target priority areas for conservation (Walker & al., 2021). However, the magnitude of the task seems insurmountable, as even listing the organisms already known to science is a challenge (Nic Lughadha & al., 2016; Borsch & al., 2020; Govaerts & al., 2021). A new approach is needed to overcome this stumbling block and scale-up the assessment of extinction risk. Here we show that analyses of natural history mega-datasets using artificial intelligence allows us to predict a baseline conservation status for all vascular plants and identify target areas for conservation corresponding to hotspots optimally capturing different aspects of biodiversity. We illustrate the strong potential of AI-based methods to reliably predict extinction risk on a global scale. Our approach not only retrieved recognized biodiversity hotspots but identified new areas that may guide future global conservation action (Myers & al., 2000; Brooks & al., 2006). To further work in this area and guide the targets of the post-2020 biodiversity framework (Díaz & al., 2020a; Secretariat of the United Nations Convention on Biological Diversity, 2020; Mair & al., 2021), it will be necessary to accelerate the acquisition of fundamental data and allow inclusion of social and economic factors (Possingham & Wilson, 2005).

International audience; Disentangling the multiple factors controlling species diversity is a major challenge in ecology. Island biogeography and environmental filtering are two influential theories emphasizing respectively island size and isolation, and the abiotic environment, as key drivers of species richness. However, few attempts have been made to quantify their relative importance and investigate their mechanistic basis. Here, we applied structural equation modelling, a powerful method allowing test of complex hypotheses involving multiple and indirect effects, on an island-like system of 22 French Guianan neotropical inselbergs covered with rock-savanna. We separated the effects of size (rock-savanna area), isolation (density of surrounding inselbergs), environmental filtering (rainfall, altitude) and dispersal filtering (forest-matrix openness) on the species richness of all plants and of various ecological groups (terrestrial versus epiphytic, small-scale versus large-scale dispersal species). We showed that the species richness of all plants and terrestrial species was mainly explained by the size of rock-savanna vegetation patches, with increasing richness associated with higher rock-savanna area, while inselberg isolation and forest-matrix openness had no measurable effect. This size effect was mediated by an increase in terrestrial-habitat diversity, even after accounting for increased sampling effort. The richness of epiphytic species was mainly explained by environmental filtering, with a positive effect of rainfall and altitude, but also by a positive size effect mediated by enhanced woody-plant species richness. Inselberg size and environmental filtering both explained the richness of small-scale and large-scale dispersal species, but these ecological groups responded in opposite directions to altitude and rainfall, that is positively for large-scale and negatively for small-scale dispersal species. Our study revealed both habitat diversity associated with island size and environmental filtering as major drivers of neotropical inselberg plant diversity and showed the importance of plant species growth form and dispersal ability to explain the relative importance of each driver.

International audience; The nearly cosmopolitan tribe Desmodieae (Fabaceae) includes many important genera for medicine and forage. However, the phylogenetic relationships among the infratribal groups circumscribed using morphological traits are still poorly known. In this study, we used chloroplast (rbcL, psbA-trnH) and nuclear (ITS-1) DNA sequences to investigate the molecular phylogeny and historical biogeography of Desmodieae, and infer ancestral states for several vegetative and reproductive traits. Three groups, corresponding to the Desmodium, Lespedeza, and Phyllodium groups sensu Ohashi were retrieved in the phylogenetic analyses. Conflicts in the topologies inferred from the chloroplast and nuclear datasets were detected. For instance, the Lespedeza clade was sister to the groups Phyllodium + Desmodium based on chloroplast DNA, but nested within the Desmodium group based on ITS-1. Moreover, the New Caledonian endemic genera Arthroclianthus and Nephrodesmus were not monophyletic but together formed a clade, which also included Hanslia and Ohwia based on chloroplast DNA. The hypothetical common ancestor of Desmodieae was dated to the Middle Oligocene (ca. 28.3 Ma) and was likely an Asian shrub or tree producing indehiscent loments. Several colonization events towards Oceania, America, and Africa occurred (all less than ca. 17.5 Ma), most probably through long distance dispersal. The fruits of Desmodieae repeatedly evolved from indehiscence to dehiscence. We also showed that indehiscent loments allow for more variability in the number of seeds per fruit than indehiscent legumes. Modularity seems here to allow variability in the number of ovules produced in a single ovary.

International audience; Granitic outcrop vegetation was compared in 22 inselbergs of French Guiana, South America, using RLQ and fourth-corner analyses to identify the main relationships between environmental gradients and plant traits. At the scale of the whole territory the distribution of species and species traits was mostly driven by a spatially-structured gradient embracing regional climate (annual rainfall), forest matrix (canopy openness), and inselberg features (altitude, shape, habitats, summit forest, degree of epiphytism, fire events). Biogeographic, environmental and past historical factors contribute to explain the variation observed at coarse scale and two groups of inselbergs are identified. A first group occupies the southern peneplain in a semi-open forest matrix and exhibits a higher representation of suffrutescent species and climbers, a lower representation of upright shrubs, a lower degree of Guiana Shield endemism, and a higher incidence of human use and autochory. All these features suggest an adaptation to more disturbed environments linked to past climate changes and savannization and to human influences. A second group, characterized by opposite plant traits, occupies the northern part of French Guiana and the far south within a closed forest matrix. Within archipelagos (inselbergs at less than 7 km distance), C-score and Mantel tests revealed a random co-occurrence of plant species and an increase of floristic dissimilarity with distance without any concomitant change in plant traits, respectively, suggesting that spatially-structured stochastic factors (limitation by dispersal) were the driving force of vegetation change at fine scale.