Higher colonization pressure increases the risk of sustaining invasion by invasive non-indigenous species
Bibliographic record
Abstract
Considerable attention has been focused on the concept of Propagule Pressure (number of individuals introduced and introduction events) as a predictor of invasion success (975 papers). Much less well studied is the role of Colonization Pressure (number of species introduced; 24 studies), the complement of propagule pressure. Here we review the invasion history of the Laurentian Great Lakes to predict the risk of a future invasive (i.e. producing adverse ecological effects on other species) non-indigenous species based upon the number of species introduced (colonization pressure), using the recorded history of invasions in this system as our starting point. Historically, 52% of the fishes that were introduced and became established in the Great Lakes were subsequently identified in the literature as invasive, whereas the value for invertebrates (16%) was much lower. Assuming future invaders have similar invasion attributes as those already present, the risk of getting at least one high impact species is positively and asymptotically related to the number of species introduced, though the rate is substantially higher for fishes than for invertebrates. Our study provides support for the contention that managers ought to focus initially on vectors transmitting multiple species when attempting to prevent invasion of their system by species likely to become problematic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".