Data from: Plant invasion alters trait composition and diversity across habitats
Bibliographic record
Abstract
Increased globalization has accelerated the movement of species around the world. Many of these non-native species have the potential to profoundly alter ecosystems. The mechanisms underpinning this impact are often poorly understood, and traits are often overlooked when trying to understand and predict the impacts of species invasions on communities. We conducted an observational field experiment in Canada’s first National Urban Park, where we collected trait data for seven different functional traits across an abundance gradient of the invasive Vincetoxicum rossicum in open meadow and understory habitats. We assessed invasion impacts on communities, and associated mechanisms, by examining three complementary functional trait measures: Community weighted mean, range of trait values, and species’ distances to the invader in trait space. We found that V. rossicum invasion significantly altered the functional structure of herbaceous plant communities. In both habitats V. rossicum changed the community weighted means, causing invaded communities to become increasingly similar in their functional structure. In addition, V. rossicum also reduced the trait ranges for a majority of traits indicating that species are being deterministically excluded in invaded communities. Further, we observed different trends in the meadow and understory habitats: in the understory, resident species that were more similar to V. rossicum in multivariate trait space were excluded more, however this was not the case in the meadow habitat. This suggests that V. rossicum alters communities uniquely in each habitat, in part by creating a filter in which only certain resident species are able to persist. This filtering process causes a non-random reduction in species’ abundances, which in turn would be expected to alter how invaded ecosystems function. Using trait-based frameworks leads to better understanding and prediction of invasion impacts. This novel framework can also be used in restoration practices to understand how invasion impacts communities and to reassemble communities after invasive species management.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.013 |
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".