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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.

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.

Questions: Shrub vegetation has been expanding across much of the rapidly changing Arctic. Yet, there is still uncertainty about the underlying drivers of shrub community composition. Here, we use extensive vegetation surveys and a trait-based approach to answer the following questions: which abiotic and biotic factors explain abundance of shrub species and functional groups in the Arctic tundra, and can we interpret these relationships using plant traits related to resource acquisition?. Location: Nuup Kangerlua (Godthåbsfjord), western Greenland. Methods: We tested the power of nine climatic, topographic and biotic variables to explain the abundances of nine shrub species using a Bayesian hierarchical modelling framework. Results: We found highly variable responses among species and functional groups to both abiotic and biotic environmental variation. The overall most important abiotic explanatory variable was annual air temperature variability, which was highly correlated with winter minimum air temperature. Functional community composition and graminoid abundance were the most influential biotic factors. While we did not find systematic patterns between shrub abundances and abiotic variables with regard to resource acquisition traits, these traits did explain relationships between shrub abundances and biotic variables. Conclusions: Shrub abundance responses to abiotic variables rarely aligned with expectations based on plants’ resource acquisition traits or functional groups. Our results, therefore, indicate that approaches exclusively based on resource acquisition traits might be limited in their ability to predict abundances of individual groups and species, particularly in response to complex abiotic environments. However, integrating community theory and functional trait concepts represents a promising pathway to better predict biotic interactions and ultimately responses of dominant shrub vegetation to rapid environmental changes across the arctic tundra

Modelling ecological niches of migratory animals requires incorporating a temporal dimension, in addition to space. Here, we introduce an approach to model multigenerational migratory insects using time-partitioned environmental variables (by months and years) and time- and behaviour-partitioned records (breeding records to model reproductive habitat). We apply this methodology to modelling the Palearctic-African migratory cycle of the Painted Lady butterfly (Vanessa cardui), based on data encompassing 36 years (646 breeding sites from 30 countries). Each breeding record is linked to a particular time (month and year), and the associated values of the bioclimatic variables are used for an ensemble modelling strategy, to finally obtain monthly projections. The results show obligated movements, mostly latitudinal, for the species' successive generations across the overall range, and only scattered locations show high probabilities of reproduction year-round. The southernmost reproductive areas estimated for the Palearctic-African migratory pool reach equatorial latitudes from December to February. We thus propose a potential distribution for the winter ‘missing generations' that would expand the V. cardui migration cycle to encompass about 15 000 km in latitude, from northernmost Europe to equatorial Africa. In summer, Europe represents the major temporary resource for V. cardui, while January and February show the lowest overall suitability values, and they are potentially the most vulnerable period for the species to suffer yearly bottlenecks. In summary, we demonstrate the potential of the proposed niche modelling strategy to investigate migratory movements of insects.