Trait‐based ecology aims to understand the processes that generate the overarching diversity of organismal traits and their influence on ecosystem functioning. Achieving this goal requires simplifying this complexity in synthetic axes defining a trait space and to cluster species based on their traits while identifying those with unique combinations of traits. However, so far, we know little about the dimensionality, the robustness to trait omission and the structure of these trait spaces. Here, we propose a unified framework and a synthesis across 30 trait datasets representing a broad variety of taxa, ecosystems and spatial scales to show that a common trade‐off between trait space quality and operationality appears between three and six dimensions. The robustness to trait omission is generally low but highly variable among datasets. We also highlight invariant scaling relationships, whatever organismal complexity, between the number of clusters, the number of species in the dominant cluster and the number of unique species with total species richness. When species richness increases, the number of unique species saturates, whereas species tend to disproportionately pack in the richest cluster. Based on these results, we propose some rules of thumb to build species trait spaces and estimate subsequent functional diversity indices.

Emphasis has been put in recent ecological research on investigating phylogenetic, functional and taxonomic facets of biological diversity. While a flourishing number of indices have been proposed for assessing functional diversity, surprisingly few options are available to characterize functional rarity. Functional rarity can play a key role in community and ecosystem dynamics. We introduce here the funrar R package to quantify functional rarity based on species trait differences and species frequencies at local and regional scales. Because of the increasing availability of big datasets in macroecology and biogeography, we optimized funrar to work with large datasets of thousands of species and sites. We illustrate the use of the package to investigate the functional rarity of North and Central American mammals. K E Y W O R D Sbiodiversity, biodiversity indices, functional biogeography, functional trait, R package, rarity

Aim Leaf traits strongly impact biogeochemical cycles in terrestrial ecosystems. Understanding leaf trait variation along environmental gradients is thus essential to improve the representation of vegetation in Earth system models. Our aims were to quantify relationships between leaf traits and climate in permanent grasslands at a biogeographical scale and to test whether these relationships were sensitive to (a) the level of nitrogen inputs and (b) the inclusion of information pertaining to plant community organization. Location Permanent grasslands throughout France. Methods We combined existing datasets on climate, soil, nitrogen inputs (fertilization and deposition), species composition and four traits, namely specific leaf area, leaf dry matter content and leaf nitrogen and phosphorus concentrations, for 15,865 French permanent grasslands. Trait–climate relationships were tested using the following four climatic variables available across 1,833 pixels (5 km × 5 km): mean annual temperature (MAT) and precipitation (MAP), and two indices accounting for the length of the growing season. We compared these relationships at the pixel level using either using community-level or species’ trait means. Results Our findings were as follows: (a) leaf traits related to plant nutrient economy shift consistently along a gradient of growing season length accounting for temperature and soil water limitations of plant growth (GSLtw); (b) weighting leaf traits by species abundance in local communities is pivotal to capture leaf trait–environment relationships correctly at a biogeographical scale; and (c) the relationships between traits and GSLtw weaken for grasslands with a high nitrogen input. Main conclusions The effects of climate on plant communities are better described using composite descriptors than coarse variables such as MAT or MAP, but appear weaker for high-nitrogen grasslands. Using information at the community level tends to strengthen trait–climate relationships. The interplay of land management, community assembly and bioclimate appears crucial to the prediction of leaf trait variations and their effects on biogeochemical cycles

We introduce the R package ecolottery dedicated to quick and efficient simulation of communities undergoing local neutral dynamics with environmentally filtered immigration from a reference species pool (spatially implicit model). The package includes an Approximate Bayesian Computation (ABC) tool to estimate the parameters of these processes. We present the rationale of the approach and show examples of simulations and ABC analysis. The species in the reference pool differ in their abundances and trait values. Environmental filtering weights the probability of immigration success depending on trait values, while the descendants of established immigrants undergo neutral stochastic drift. The reference pool can be defined in a flexible way as representing, e.g. the composition of a broad biogeographical region, or available dispersers around local communities. The package provides a process‐based alternative to the use of randomization‐based null models. The package proposes a coalescent‐based simulation algorithm that presents significant advantages over alternative algorithms. It does not require simulating community dynamics from an initial state forward in time but does still allow measurement of the influence of environmental filtering. Because of its high calculation speed, this approach allows simulating many communities within a reasonable amount of time. Diverse patterns of taxonomic, functional and phylogenetic compositions can be generated. The package can be used to explore the outcome of ecological and evolutionary processes playing at local and regional scales, and to estimate the parameters of these processes based on observed patterns.

Premise of the Study Despite long‐term research efforts, a comprehensive perspective on the ecological and functional properties determining plant weediness is still lacking. We investigated here key functional attributes of arable weeds compared to non‐weed plants, at large spatial scale. Methods We used an intensive survey of plant communities in cultivated and non‐cultivated habitats to define a pool of plants occurring in arable fields (weeds) and one of plants occurring only in open non‐arable habitats (non‐weeds) in France. We compared the two pools based on nine functional traits and three functional spaces (LHS, reproductive and resource requirement hypervolumes). Within the weed pool, we quantified the trait variation of weeds along a continuum of specialization to arable fields. Key Results Weeds were mostly therophytes and had higher specific leaf area, earlier and longer flowering, and higher affinity for nutrient‐rich, sunny and dry environments compared to non‐weeds, although functional spaces of weeds and non‐weeds largely overlapped. When fidelity to arable fields increased, the spectrum of weed ecological strategies decreased as did the overlap with non‐weeds, especially for the resource requirement hypervolume. Conclusions Arable weeds constitute a delimited pool defined by a trait syndrome providing tolerance to the ecological filters of arable fields (notably, regular soil disturbances and fertilization). The identification of such a syndrome is of great interest to predict the weedy potential of newly established alien plants. An important reservoir of plants may also become weeds after changes in agricultural practices, considering the large overlap between weeds and non‐weeds.

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Aim The characterization of trait–environment relationships over broad‐scale gradients is a critical goal for ecology and biogeography. This implies the merging of plot and trait databases to assess community‐level trait‐based statistics. Potential shortcomings and limitations of this approach are that: (i) species traits are not measured where the community is sampled and (ii) the availability of trait data varies considerably across species and plots. Here we address the effect of trait data representativeness [the sampling effort per species and per plot] on the accuracy of (i) species‐level and (ii) community‐level trait estimates and (iii) the consequences for the shape and strength of trait–environment relationships across communities. Innovation We combined information existing in databases of vegetation plots and plant traits to estimate community‐weighted means [CWMs] of four key traits [specific leaf area, plant height, seed mass and leaf nitrogen content per dry mass] in permanent grasslands at a country‐wide scale. We propose a generic approach for systematic sensitivity analyses based on random subsampling and data reduction to address the representativeness of incomplete and heterogeneous trait information when exploring trait–environment relationships across communities. Main conclusions The accuracy of the CWMs was little affected by the number of individual trait values per species [NIV] but strongly affected by the cover proportion of species with available trait values [PCover]. A PCover above 80% was required for all four traits studied to obtain an estimation bias below 5%. Our approach therefore provides more conservative criteria than previously proposed. Restrictive criteria on both NIV and PCover primarily excluded communities in harsh environments, and such reduction of the sampled gradient weakened trait–environment relationships. These findings advocate systematic measurement campaigns in natural environments to increase species coverage in global trait databases, with special emphasis on species occurring in under‐sampled and harsh environmental conditions.

Despite the paramount role of plant diversity for ecosystem functioning, biogeochemical cycles, and human welfare, knowledge of its global distribution is still incomplete, hampering basic research and biodiversity conservation.Here, we used machine learning (random forests, extreme gradient boosting, and neural networks) and conventional statistical methods (generalized linear models and generalized additive models) to test environment-related hypotheses of broad-scale vascular plant diversity gradients and to model and predict species richness and phylogenetic richness worldwide. To this end, we used 830 regional plant inventories including c. 300 000 species and predictors of past and present environmental conditions.Machine learning showed a superior performance, explaining up to 80.9% of species richness and 83.3% of phylogenetic richness, illustrating the great potential of such techniques for disentangling complex and interacting associations between the environment and plant diversity. Current climate and environmental heterogeneity emerged as the primary drivers, while past environmental conditions left only small but detectable imprints on plant diversity.Finally, we combined predictions from multiple modeling techniques (ensemble predictions) to reveal global patterns and centers of plant diversity at multiple resolutions down to 7774 km 2 . Our predictive maps provide accurate estimates of global plant diversity available at grain sizes relevant for conservation and macroecology.