Functional traits modulate the response of alien plants along abiotic and biotic gradients
Bibliographic record
Abstract
Abstract Aim It is widely recognized that the prediction of invasion success at large biogeographical scales requires jointly accounting for alien species traits and local community filters, such as abiotic conditions, biotic interactions and propagule pressure. Despite this recognition, interactions between traits and community filters are generally neglected. Here, we aim to address this limitation by developing a hierarchical framework that builds on trait‐based theory to model occurrences of alien species as a function of spatially explicit variables, filtering invasions and their interactions with species traits. Location Herbaceous communities throughout France. Time period c. 1960–2012 (mostly after 1990). Major taxa studied Herbaceous plants. Methods Based on a large dataset of >50,000 community plots, we built a multispecies hierarchical model of the distribution of the 10 most widespread alien plants in French grasslands. In this model, we explicitly account for how plant height, specific leaf area (SLA) and seed mass affect the occurrence of alien species along gradients of human pressure, environmental conditions and native community composition. Finally, we contrast the results to native species responses along the same gradients. Results We show that two out of three traits significantly modulate the responses of species along these broad gradients. Alien plants with exploitative traits (i.e., tall and with high SLA) were less dependent on human pressure, more efficient in resource‐rich environments and better at avoiding competition from native species. These trait–gradient interactions were often unique to alien plants (e.g., human pressure was important only for supporting alien species with low SLA), even though trait ranges of alien and native species were comparable. Ultimately, the modelling of trait–gradient interactions allows spatially explicit estimations of invasion risks by novel species with particular sets of traits. Main conclusions By taking the best from multispecies distribution modelling and trait‐based theory, our framework paves the way for a generalized mechanistic understanding of how traits influence the success of alien plants and their spatial distributions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".