No consistent pollinator‐mediated impacts of alien plants on natives
Bibliographic record
Abstract
The introduction of an alien plant is widely assumed to have negative consequences for the pollinator-mediated fitness of nearby natives. Indeed, a number of studies, including a highly cited meta-analysis, have concluded that the trend for such interactions is competitive. Here we provide evidence that publication bias and study design have obscured our ability to assess the pollinator-mediated impacts of alien plants. In a meta-analysis of 76 studies, we demonstrate that alien/native status does not predict the outcome of pollinator-mediated interactions among plants. Moreover, we found no evidence that similarity in floral traits or phylogenetic distance between species pairs influences the outcome of pollinator-mediated interactions. Instead, we report that aspects of study design, such as distance between the control and nearest neighbour, and/or the arrangement of study plants better predict the impact of a neighbour than does alien/native status. Our study sheds new light on the role that publication bias and experimental design play in the evaluation of key patterns in ecology. We conclude that, due to the absence of clear, generalisable pollinator-mediated impacts of alien species, management schemes should base decisions on community-wide assessments of the impacts of individual alien plant species, and not solely on alien/native status itself.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".