Facilitative interactions among aquatic invaders: is an "invasional meltdown" occurring in the Great Lakes?
Bibliographic record
Abstract
A widely cited hypothesis in ecology is that species-rich communities are less vulnerable to invasion than species-poor ones, owing to competition for limiting resources (the "biotic resistance" model). However, evidence for biotic resistance in aquatic ecosystems is equivocal. Contrary to the view that communities become more resistant to invasion as they accumulate species, the rate of invasion has increased over the past century in areas that have received frequent shipping traffic. Furthermore, introduced species may facilitate, rather than compete with, one another. A review of invasions in the Great Lakes indicates that direct positive (mutualistic and commensal) interactions among introduced species are more common than purely negative (competitive and amensal) interactions. In addition, many exploitative (e.g., predatorprey) interactions appear to be strongly asymmetric in benefiting one invading species at a negligible cost to another. These observations, combined with an increasing invasion rate in the Great Lakes, tentatively support the Simberloff Von Holle "invasional meltdown" model. The model posits that ecosystems become more easily invaded as the cumulative number of species introductions increases, and that facilitative interactions can exacerbate the impact of invaders. It provides a theoretical argument for substantially reducing the rate of species introductions to the Great Lakes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 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".