Progress toward understanding the ecological impacts of nonnative species
Bibliographic record
Abstract
A predictive understanding of the ecological impacts of nonnative species has been slow to develop, owing largely to an apparent dearth of clearly defined hypotheses and the lack of a broad theoretical framework. The context dependency of impact has fueled the perception that meaningful generalizations are nonexistent. Here, we identified and reviewed 19 testable hypotheses that explain temporal and spatial variation in impact. Despite poor validation of most hypotheses to date, evidence suggests that each can explain at least some impacts in some situations. Several hypotheses are broad in scope (applying to plants and animals in virtually all contexts) and some of them, intriguingly, link processes of colonization and impact. Collectively, these hypotheses highlight the importance of the functional ecology of the nonnative species and the structure, diversity, and evolutionary experience of the recipient community as general determinants of impact; thus, they could provide the foundation for a theoretical framework for understanding and predicting impact. Further substantive progress toward this goal requires explicit consideration of within‐taxon and across‐taxa variation in the per capita effect of invaders, and analyses of complex interactions between invaders and their biotic and abiotic environments.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".