Biotic interactions and plant invasions
Bibliographic record
Abstract
Introduced plant populations lose interactions with enemies, mutualists and competitors from their native ranges, and gain interactions with new species, under new abiotic conditions. From a biogeographical perspective, differences in the assemblage of interacting species, as well as in abiotic conditions, may explain the demographic success of the introduced plant populations relative to conspecifics in their native range. Within invaded communities, the new interactions and conditions experienced by the invader may influence both its demographic success and its effects on native biodiversity. Here, we examine indirect effects involving enemies, mutualists and competitors of introduced plants, and effects of abiotic conditions on biotic interactions. We then synthesize ideas building on Darwin's idea that the kinds of new interactions gained by an introduced population will depend on its relatedness to native populations. This yields a heuristic framework to explain how biotic interactions and abiotic conditions influence invader success. We conclude that species introductions generally alter plants' interactions with enemies, mutualists and competitors, and that there is increasing evidence that these altered interactions jointly influence the success of introduced populations.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".