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The ever‐increasing and expanding globalisation of trade and transport underpins the escalating global problem of biological invasions. Developing biosecurity infrastructures is crucial to anticipate and prevent the transport and introduction of invasive alien species. Still, robust and defensible forecasts of potential invaders are rare, especially for species without known invasion history. Here, we aim to support decision‐making by developing a quantitative invasion risk assessment tool based on invasion syndromes (i.e., generalising typical attributes of invasive alien species). We implemented a workflow based on 'Multiple Imputation with Chain Equation' to estimate invasion syndromes from imputed datasets of species' life‐history and ecological traits and macroecological patterns. Importantly, our models disentangle the factors explaining (i) transport and introduction and (ii) establishment. We showcase our tool by modelling the invasion syndromes of 466 amphibians and reptile species with invasion history. Then, we project these models to amphibians and reptiles worldwide (16,236 species [c.76% global coverage]) to identify species with a risk of being unintentionally transported and introduced, and risk of establishing alien populations. Our invasion syndrome models showed high predictive accuracy with a good balance between specificity and generality. Unintentionally transported and introduced species tend to be common and thrive well in human‐disturbed habitats. In contrast, those with established alien populations tend to be large‐sized, are habitat generalists, thrive well in human‐disturbed habitats, and have large native geographic ranges. We forecast that 160 amphibians and reptiles without known invasion history could be unintentionally transported and introduced in the future. Among them, 57 species have a high risk of establishing alien populations. Our reliable, reproducible, transferable, statistically robust and scientifically defensible quantitative invasion risk assessment tool is a significant new addition to the suite of decision‐support tools needed for developing a future‐proof preventative biosecurity globally. [ABSTRACT FROM AUTHOR]

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.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/).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.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. [ABSTRACT FROM AUTHOR]

Beyond the local abundance of species, their functional trait distinctiveness is now recognized as a key driver of community dynamics and ecosystem functioning. Yet, since the functional distinctiveness of a species is always relative to a given species pool, a species distinct at regional scale might not necessarily be distinct at local or community scale, and reciprocally. To assess the importance of scale (i.e. the definition of a species pool) when quantifying the functional distinctiveness of species, and how it might distort the ecological conclusions derived from it, we quantified trait distinctiveness of 1350 plant species at regional, local and community scales over ca 88 000 grassland plots in France. We measured differences in functional distinctiveness of species between regional (mainland France), local (10 × 10 km cell) and community (10 × 10 m plot) scale, and tested the influence of environmental predictors (climate and nitrogen input) and contexts (environmental distinctiveness, frequency and heterogeneity) on these variations. In line with theoretical expectations, we found large variation in functional distinctiveness (in particular between regional and community scales) for many species, with a general tendency of lower distinctiveness at smaller scales. We also showed that nitrogen input – a key aspect of high land use intensity – and environmental frequency partly explained the differences between local and regional scale only. These results suggest the role played by environmental filtering on species distinctiveness at local scale, but the determinant of distinctiveness variations at community scale still need to be elucidated. Our study provides robust empirical evidence that measures of ecological originality are strongly scale‐dependent. We urge ecologists to carefully consider the scale at which they measure distinctiveness, as ignoring scale dependencies could lead to biased (or even entirely wrong) conclusions when not considered at the scale of interest for the respective research question. [ABSTRACT FROM AUTHOR]

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Abstract: 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. [ABSTRACT FROM AUTHOR]