Taxonomic diversity effects on forest productivity and response to climate extremes range from positive to negative, suggesting a key role for complex interactions among neighbouring trees. To elucidate how neutral interactions, hierarchical competition and resource partitioning between neighbours’ shape tree growth and climate response in a highly diverse Amazonian forest, we combined 30 years of tree censuses with measurements of water‐ and carbon‐related traits. We modelled individual tree growth response to climate and neighbourhood to disentangle the relative effect of neighbourhood densities, trait hierarchies and dissimilarities. While neighbourhood densities consistently decreased growth, trait dissimilarity increased it, and both had the potential to influence climate response. Greater water conservatism provided a competitive advantage to focal trees in normal years, but water–spender neighbours reduced this effect in dry years. By underlining the importance of density and trait‐mediated neighbourhood interactions, our study offers a way towards improving predictions of forest dynamics.

The biodiversity of tropical rainforest is difficult to assess. Yet, its estimation is necessary for conservation purposes, to evaluate our level of knowledge and the risks faced by the forest in relation to global change. Our contribution is to estimate the regional richness of tree species from local but widely spread inventories. We reviewed the methods available, which are nonparametric estimators based on abundance or occurrence data, log-series extrapolation and the universal species–area relationship based on maximum entropy. Appropriate methods depend on the scale considered. Harte’s self-similarity model is suitable at the regional scale, while the log-series extrapolation is not. GuyaDiv is a network of forest plots installed over the whole territory of French Guiana, where trees over 10 cm DBH are identified. We used its information (1315 species censused in 68 one-hectare plots) to estimate the exponent of the species–area relationship, assuming Arrhenius’s power law. We could then extrapolate the number of species from three local, wide inventories (over 2.5 km2). We evaluated the number of tree species around 2200 over the territory.

Over the last decade, distance-based methods have been introduced and then improved in the field of spatial economics to gauge the geographic concentration of activities. There is a growing literature on this theme including new tools, discussions on their specific properties and various applications. However, there is currently no typology of distance-based methods. This paper fills that gap. The proposed classification helps understand all the properties of distance-based methods and proves that they are variations on the same framework.

Measuring functional or phylogenetic diversity is the object of an active literature. The main issues to address are relating measures to a clear conceptual framework, allowing unavoidable estimation-bias correction and decomposing diversity along spatial scales. We provide a general mathematical framework to decompose measures of species-neutral, phylogenetic or functional diversity into $\alpha$ and $\beta$ components. We first unify the definitions of phylogenetic and functional entropy and diversity as a generalization of HCDT entropy and Hill numbers when an ultrametric tree is considered. We then derive the decomposition of diversity. We propose a bias correction of the estimates allowing meaningful computation from real, often undersampled communities. Entropy can be transformed into true diversity, that is an effective number of species or communities. Estimators of $\alpha$- and $\beta$-entropy, phylogenetic and functional entropy are provided. Proper definition and estimation of diversity is the first step towards better understanding its underlying ecological and evolutionary mechanisms.

Existing literature reviews on the impact of logging on the biodiversity of tropical forests cover a wide range of plant and animal taxa, but few focus on tree diversity. We performed a systematic review to compile our present scientific knowledge on the impact of selective logging on tree species diversity. This knowledge is essential for defining sustainability practices in the management of tropical forests, and this systematic review aims to fill this gap. We extracted tree diversity from the selected studies and then quantified the effect of logging intensity and recovery time on the relative tree diversity using weighted mixed-effects models. Tree diversity recovery after selective logging was positively associated with time since logging and negatively with logging intensity (adjusted R2 = 0.43). At low to moderate intensities (20%–40% basal area removal), diversity started at 2%–8% below reference levels just after logging and recovered to around 1%–8% above unlogged forests within 20–30 years. At high intensities (70% removal), diversity remained 5%–14% below reference levels at 20 years, and only approached unlogged levels after about 30 years. This finding aligns with the intermediate disturbance hypothesis, which predicts that moderate disturbances are associated with higher diversity through the coexistence of pioneer and late-successional species. Adopting reduced impact logging, limiting logging intensity, and extending rotation cycles emerge as key practices to maintain tree diversity in managed tropical forests. This review also highlights the need for standardized long-term experimental plots to monitor post-logging floristic changes and forest dynamics. Moreover, studies on the cumulative impact of selective logging and climate change are still very scarce. Special efforts should be dedicated to this issue, as climate change can indeed affect the diversity and composition of tropical forests.

Since agglomeration is a core question in regional science, spatial concentration measures are widely employed in that field to evaluate the spatial distribution of activities. Increasing access to large geo-referenced datasets, coupled with the development of computing power, has encouraged the search for suitable spatial statistical tools. Distance-based methods have been extensively developed to detect spatial concentration, dispersion or independence of entities at any distance and without any bias. Today, distance-based methods face a new challenge: they must be able to address very large micro-geographic datasets. Recently, Tidu et al. (2024) highlighted the qualities of Marcon and Puech’s M function, a relative distance-based measure, and also expressed reservations about the computation time required. Herein, we explore two possible ways to reduce the computation burden of large geo-located datasets: approximating the position of points and thinning the point pattern. In both cases, the deterioration extent of the M results is estimated and discussed as the gains it provides in computation time, using the R software. We discuss implications of these findings in the field of regional science. We notably provide evidence that the individual location approximation generates information loss at substantially small distances, implying a trade-off between the smallest distance at which spatial interactions could be detected and computing performance. We also give support that random thinning is an efficient method to analyze large datasets with very good accuracy. The R code used in the article is given for the reproducibility of our results.

Tropical rainforests are among the most emblematic ecosystems in terms of biodiversity. However, our understanding of the structure of tropical biodiversity is still incomplete, particularly for certain groups of soil organisms such as earthworms, whose importance for ecosystem functioning is widely recognised. This study aims at determining the relative contribution of alpha and beta components to earthworm regional diversity at a hierarchy of nested spatial scales in natural ecosystems of French Guiana. For this, we performed a hierarchical diversity partitioning of a large dataset on earthworm communities, in which DNA barcode‐based operational taxonomic units (OTUs) were used as species surrogates. Observed regional diversity comprised 256 OTUs. We found that alpha diversity was lower than predicted by chance, regardless of the scale considered. Community‐scale alpha diversity was on average 7 OTUs. Beta diversity among remote landscapes was higher than expected by chance, explaining as much as 87% of regional diversity. This points to regional mechanisms as the main driver of species diversity distribution in this group of organisms with low dispersal capacity. At more local scales, multiplicative beta diversity was higher than expected by chance between habitats, while it was lower than expected by chance between communities in the same habitat. This highlights the local effect of environmental filters on the species composition of communities. The calculation of a Chao 2 index predicts that as many as 1700 species could be present in French Guiana, which represents a spectacular increase compared with available checklists, and calls into question the commonly accepted estimates of global number of earthworm species.

LiDAR makes it possible to describe the 3D structure of the forest, from which species habitats can be accurately estimated, over large areas at fine resolution. However, standard airborne laser scanning (ALS) fails to describe the lower canopy in sufficient detail due to occlusion by the upper canopy. The understory is important to characterise as it harbours the majority of the forest community and is the place where regeneration takes place. Here we explored the potential of low altitude high power ALS with enhanced penetration, and denser UAV LiDAR (Unmanned Aerial Vehicle) to describe the structure of the understory. We used the recorded laser pulse extinction to build a 3D model of light transmission through the canopy. We evaluated the capacity of the light transmission model to estimate the spatial and angular variation of light in the tropical understory, considering different leaf inclination distribution functions (LIDF), compared with measurements from two different field sensors. We found that (i) LiDAR can be used to estimate the light environment in the understory in a spatially and angularly consistent way; (ii) high pulse density does not guarantee an accurate characterization of the forest structure, and penetration rate is an important characteristic to accurately describe the forest structure, especially the understory; (iii) taking into account the anisotropic nature of light transmittance improved the estimation of absolute light levels by radiative transfer.

Species’ traits and environmental conditions determine the abundance of tree species across the globe. The extent to which traits of dominant and rare tree species differ remains untested across a broad environmental range, limiting our understanding of how species traits and the environment shape forest functional composition. We use a global dataset of tree composition of >22,000 forest plots and 11 traits of 1663 tree species to ask how locally dominant and rare species differ in their trait values, and how these differences are driven by climatic gradients in temperature and water availability in forest biomes across the globe. We find three consistent trait differences between locally dominant and rare species across all biomes; dominant species are taller, have softer wood and higher loading on the multivariate stem strategy axis (related to narrow tracheids and thick bark). The difference between traits of dominant and rare species is more strongly driven by temperature compared to water availability, as temperature might affect a larger number of traits. Therefore, climate change driven global temperature rise may have a strong effect on trait differences between dominant and rare tree species and may lead to changes in species abundances and therefore strong community reassembly.