Bibliographic record
Abstract
Species invasion represents one of the major drivers of biodiversity change globally, yet there is widespread scientific and popular confusion and controversy about the nature of non-indigenous species (NIS) impact. This confusion stems from differing notions and understanding of what constitutes invasive species ‘impact’ and the scales at which it should be assessed. I argue that the proximate mechanisms determining invasive species impact happen at smaller scales where species interact, and by understanding these mechanisms, we can scale up to a broader understanding of how invasive species impact biodiversity. The mechanisms of NIS impact on potential competitors can be classified into four scenarios: 1) minimal impact from NIS inhabiting unique niche space; 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 ostensibly caused by ecosystem modification that filters out other species. I develop a statistical test to distinguish these four mechanisms based on community rank-abundance curves. Using an example dataset from plant communities invaded by the dominant invasive vine, Vincetoxicum rossicum, I show that in long-term plots that had high native plant diversity and where V. rossicum increased, impact resulted in either targeted extirpations (scenario 3) or widespread biodiversity loss (scenario 4). Regardless of whether 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.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".