Mutualism between co-introduced species facilitates invasion and alters plant community structure
Bibliographic record
Abstract
Generalized mutualisms are often predicted to be resilient to changes in partner identity. Variation in mutualism-related traits between native and invasive species however, can exacerbate the spread of invasive species ('invasional meltdown') if invasive partners strongly interact. Here we show how invasion by a seed-dispersing ant (Myrmica rubra) promotes recruitment of a co-introduced invasive over native ant-dispersed (myrmecochorous) plants. We created experimental communities of invasive (M. rubra) or native ants (Aphaenogaster rudis) and invasive and native plants and measured seed dispersal and plant recruitment. In our mesocosms, and in laboratory and field trials, M. rubra acted as a superior seed disperser relative to the native ant. By contrast, previous studies have found that invasive ants are often poor seed dispersers compared with native ants. Despite belonging to the same behavioural guild, seed-dispersing ants were not functionally redundant. Instead, native and invasive ants had strongly divergent effects on plant communities: the invasive plant dominated in the presence of the invasive ant and the native plants dominated in the presence of the native ant. Community changes were not due to preferences for coevolved partners: variation in functional traits of linked partners drove differences. Here, we show that strongly interacting introduced mutualists can be major drivers of ecological change.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".