Traits and biogeography are key to non‐native plant abundance, but in surprising ways

Strikingly consistent differences in the traits of native and non-native species were found in all three traits examined: specific leaf area (SLA), leaf dry matter content (LDMC), leaf N concentration (Leaf N). Blumenthal et al. address elegantly simple questions: does simply being non-native underpin a species' abundance in plant communities, or are some species set up for success by possessing traits associated with rapidly acquiring and cycling nutrients? Or is it a combination of both origin and traits which shape abundance? To test these ideas, the authors compile and harmonise data on native and non-native species abundance from almost 70 000 vegetation plots and species inventories across five ecoregions of the continental United States. This alone constitutes a major achievement and is pivotal to the strength of their findings that non-native species have distinct patterns of abundance, which are not explained by their traits. Data on patterns of plant abundance that span a wide range of different environmental contexts – from forests in the northern and eastern regions of the United States, to the central plains and southern deserts – are essential for confident predictions of overarching patterns. The global literature on invasion biology repeatedly points to the importance of site-specific context in explaining why some species thrive and become problematic (Catford et al., 2022). Using large datasets to understand abundance allows patterns to rise above the idiosyncrasies inherent to particular locations and their ecological context when searching for generalities. While certainly not the first study to make use of large datasets in invasion biology (e.g. see Seabloom et al., 2015; Pyšek et al., 2017), Blumenthal et al.'s work adds depth to our understanding of how traits and origin interact to shape abundance. Strikingly consistent differences in the traits of native and non-native species were found in all three traits examined: specific leaf area (SLA), leaf dry matter content (LDMC) and leaf N concentration (Leaf N). These traits index different aspects of plant strategy variation along the leaf economic spectrum (LES). The LES is a means for mapping how plants vary their leaf investment along a single central axis that runs from acquisitive strategies at one extreme (faster leaf turnover and lower construction costs) to conservative strategies at the other (expensive to construct, in terms of key nutrients, but long-lasting leaves; Wright et al., 2004). Blumenthal et al. show that non-native species in the United States are consistently more acquisitive in their strategy by a wide margin (i.e. SLA and Leaf N were higher, and LDMC lower) relative to native species in 77–86% of the plots studied. Importantly, however, non-native species with these 'fast' traits are more abundant where the community they inhabit is also characterised as acquisitive. These findings imply that to limit the impact of non-native species, they should be deprived of disturbance and the subsequent chances to colonise, lending empirical support to management practices that are already widely used and that limit soil disturbance, such as low-tilling rates, mulching, or manual removal (where feasible at the early stages of the invasion continuum; Poland et al., 2021). We have come a long way over the past three decades in corralling the resources needed to address ecological questions on such impressive scales. Studies like this one, and others, which span countries, continents or the world, are now routine (e.g. Díaz et al., 2016; Bruelheide et al., 2018). The insights which come from large synthesis studies are a testament to the investment of time and effort of ecologists and plant scientists the world over – in both the research sector and government agencies – in collecting, sharing and curating data on species traits and occurrence. Many millions of plant trait observations are now available across a range of sources (e.g. TRY, AusTraits, China Trait Database; Kattge et al., 2020; Falster et al., 2021; Wang et al., 2022) and some of the tools used for their curation and harmonisation are being freely shared and reused to facilitate new databasing efforts. Functional trait approaches to understanding the natural world have spread across ecology, showing that taxonomy is not the only means to understand and classify species. However, taxonomy remains the more 'complete' approach across the plant kingdom. That is, while we have estimates of key traits for many species, we are short of a comprehensive global plant trait dataset, with notable gaps in the Global South and high densities of observations in North America and Europe (Maitner et al., 2023). Whether one has a 'completist' view of trait collection will depend on the scientific question at hand. Blumenthal et al. had data available from the TRY database for only 19%, 26%, and 16% of species in their study for Leaf N, SLA, and LDMC, respectively. But as these observations were for common species, they could derive plot-based trait values across 69 441 plots for ≥ 80% of total cover. This is more than enough data to understand overarching patterns in abundance related to traits. However, it is important not to discount the need to understand the ecology of rare species, which may possess trait constellations which can inform us about diversity, invasion dynamics, or extinction risk, and help set conservation and restoration priorities that avoid homogenisation of the global flora. There are good reasons to 'chase the tail' of the distribution of key plant traits to inform rare species management. We must also seek to understand intraspecific variation in traits, which may be particularly important in the context of invasion biology (Siefert et al., 2015). Traits may shift in species as they jump geographic barriers and move to new environments, and traits may also vary along environmental gradients in response to changes in rainfall, seasonality, and temperature. Trait shifts may affect the relative abundance of species, as noted by Blumenthal et al. (see Westerband et al., 2021), and the contribution of this variation is not often considered in macroecological studies. Trait variation may also affect the efficacy of management, particularly where traits vary widely and might affect the performance of control measures, such as biocontrol that targets seed size and production. Finally, the plot-based datasets on abundance and occurrence of plant species assembled by Blumenthal et al. offer a strong foundation for future research in invasion biology. The authors have, for instance, left a door invitingly ajar to the inclusion of other species-level observations on the nature of introduction effort. Propagule pressure (i.e. the number of individuals that are introduced to found a population) is a remarkably consistent predictor of invasion success across regions and kingdoms (Cassey et al., 2018), which could partially explain the consistent pattern of higher non-native plant abundance relative to native species in the Great Plains. This US ecoregion supports a high density of agricultural land and as such is likely to have been subject to repeated introductions of non-native species as fodder and crops, as well as any accidental introductions of non-native species associated with these activities. Furthermore, the authors note that several of the most prevalent non-native plant species in their study have previously been deliberately improved through selective breeding to increase their economic benefits to people. The extent to which this kind of genetic founding effect may shape the competitiveness of non-native species, and their transition towards invasion, is yet to be synthesised at the scales relevant to creating broad generalisations. Blumenthal's dataset is well-curated and awaits new additions to dig deeper into these questions. Open access publishing facilitated by Western Sydney University, as part of the Wiley - Western Sydney University agreement via the Council of Australian University Librarians. 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