Direct and indirect effects of herbivores influencing plant invasions.
Bibliographic record
Abstract
Non-native plants rarely escape damage by herbivores. Instead, upon arrival in a new region, they begin to acquire new enemies, replacing those they have lost during their migration. These herbivores can include both natives to the new region and species that have themselves been accidentally or deliberately introduced from elsewhere, potentially including examples originating from the invader's original range. Shifts of new enemies from other hosts can occur over a range of timescales, depending in part on whether evolutionary change is required, but are likely to be faster for plants that are widespread and phylogenetically related to a herbivore's original host, and faster for generalist herbivores than for specialists. The occurrence of herbivores is not necessarily uniform across an invader's range; instead, they may be less diverse or abundant in host populations that are geographically or ecologically marginal, though existing evidence is mixed. Collectively, these new suites of herbivores can affect the growth and fitness of invaders, both directly by damaging them and indirectly by attacking their competitors. Studies comparing the demographic consequences of herbivory for successful vs unsuccessful invaders may help to clarify how often such impacts limit invasiveness. The view that an invader enters 'enemy-free' space is inaccurate; instead, persistence and spread of non-native plants often may be affected by the novel and changing assemblage of herbivores that they acquire within their new distribution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".