Quantifying and linking mechanism scenarios to invasive species impact
Bibliographic record
Abstract
Plant species invasion represents one of the major drivers of biodiversity change globally, yet there is confusion about the nature of nonindigenous species (NIS) impact. This confusion stems from differing notions of what constitutes invasive species impact and the scales at which it should be assessed. At local scales, the mechanisms of the impact on local competitors can be classified into four scenarios: (1) minimal impact from NIS inhabiting unique niches; (2) neutral impact spread across the community and proportional to NIS abundance; (3) targeted impact on a small number of competitors with overlapping niches; and (4) pervasive impact that is disproportionate to NIS abundance and caused by modifications that filter out other species. I developed a statistical test to distinguish these four mechanism scenarios based on plant community rank-abundance curves and then created a scale-independent standardized impact score. Using an example long-term dataset with high native plant diversity and an abundance gradient of the invasive vine, Vincetoxicum rossicum, I show that the impact resulted in either targeted or pervasive extirpations. Regardless of whether the NIS impact is neutral, targeted, or pervasive, the net outcome will be the homogenization of ecosystems and reduced biodiversity at larger scales, perhaps reducing ecosystem resilience. The framework and statistical evaluation of impact presented in this paper provide researchers and managers with an objective approach to quantifying NIS impact and prioritizing species for further management actions.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".