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

Identifying species that are both geographically restricted and functionally distinct, i.e. supporting rare traits and functions, is of prime importance given their risk of extinction and their potential contribution to ecosystem functioning. We use global species distributions and functional traits for birds and mammals to identify the ecologically rare species, understand their characteristics, and identify hotspots. We find that ecologically rare species are disproportionately represented in IUCN threatened categories, insufficiently covered by protected areas, and for some of them sensitive to current and future threats. While they are more abundant overall in countries with a low human development index, some countries with high human development index are also hotspots of ecological rarity, suggesting transboundary responsibility for their conservation. Altogether, these results state that more conservation emphasis should be given to ecological rarity given future environmental conditions and the need to sustain multiple ecosystem processes in the long-term.

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.

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Pollination niches are important components of ecological niches and have played a major role in the diversification of Angiosperms. In this study, we focused on Euro‐Mediterranean orchids, which use diverse pollination strategies and interact with various functional groups of insects. In these orchids, we investigated the determinants of pollination niche breadth and overlap by analysing the orchid–pollinator network and the factors that may have shaped it. We constructed a database reporting 1,278 interactions between 243 orchid and 773 pollinator species based on a thorough literature review. We then focused on 153 orchid species for which phylogenetic data were available. We used Bayesian phylogenetic mixed models to study the relationship between specialisation (as estimated by the degree and degree in the projected network), pollination strategy and breadths of orchids’ spatial and temporal distributions, while correcting for the effect of phylogenetic relationships among orchid species and sampling effort. We then used a singular value decomposition of the orchid–pollinator matrix combined to a redundancy and variation partitioning analyses to investigate the determinants of similarity in pollination niches between orchids. Specialisation was higher in deceptive than in nectar‐producing orchids and decreased with the breadth of orchids’ spatial distribution. When interactions were considered at the insect family level, similarity in pollination niches between orchids was solely explained by their pollination strategy and phylogeny. By contrast, when they were considered at the insect species level, this similarity was primarily explained by their geographical range and flowering time, although other factors had significant effects as well, with orchids using the same pollination strategy, being closely related and growing in the same habitats sharing more insect species than expected. Synthesis. Specialisation in orchid–pollinator interactions depends on orchids’ pollination strategy and geographical range. The pool of insect families with which orchids interact depends on their pollination strategy and phylogeny, with consistent associations between some functional or phylogenetic groups of orchids and some families of pollinators. By contrast, the pool of insect species with which orchids interact depends on their spatio‐temporal distribution, suggesting that at a finer scale, orchid–pollinator interactions are more opportunistic than previously thought.

1. The process of standardizing taxon names, taxonomic name harmonization, is necessary to properly merge data indexed by taxon names. The large variety of taxonomic databases and related tools are often not well described. It is often unclear which databases are actively maintained or what is the original source of taxonomic information. In addition, software to access these databases is developed following non-compatible standards, which creates additional challenges for users. As a result, taxonomic harmonization has become a major obstacle in ecological studies that seek to combine multiple datasets.2. Here, we review and categorize a set of major taxonomic databases publicly available as well as a large collection of R packages to access them and to harmonize lists of taxon names. We categorized available taxonomic databases according to their taxonomic breadth (e.g. taxon-specific vs multi-taxa) and spatial scope (e.g. regional vs global), highlighting strengths and caveats of each type of database. We divided R packages according to their function, (e.g. syntax standardization tools, access to online databases, etc.) and highlighted overlaps among them. We present our findings (e.g. network of linkages, data and tool characteristics) in a ready-to-use Shiny web application (available at: https://mgrenie.shinyapps.io/taxharmonizexplorer/).3. We also provide general guidelines and best practice principles for taxonomic name harmonization. As an illustrative example, we harmonized taxon names of one of the largest databases of community time series currently available. We showed how different workflows can be used for different goals, highlighting their strengths and weaknesses and providing practical solutions to avoid common pitfalls. 4. To our knowledge, our opinionated review represents the most exhaustive evaluation of links among and of taxonomic databases and related R tools. Finally, based on our new insights in the field, we make recommendations for users, database managers, and package developers alike.

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