Ecological similarity governs non-native fish establishment while human pressure and native diversity shape invasion richness
Bibliographic record
Abstract
Biological invasions by non-native fish species pose a major threat to global freshwater ecosystems. However, our understanding of why invaders establish in some communities but not others (i.e., occurrence probability) and why some communities harbor more invaders than others (i.e., invasion richness) remains limited. Here, we combine species- and community-level analyses across taxonomic, phylogenetic, and functional dimensions to investigate both the occurrence probability and invasion richness of exotic and translocated fish species across nearly 3000 North American freshwater fish communities. We found that ecological similarity between non-native and native species primarily governed non-native fish establishment, with invaders more likely to succeed when closely related to native species. In contrast, invasion richness was largely promoted by human pressure, while native diversity reduced richness specifically for translocated species. Additionally, both the occurrence and richness of invaders increased toward lower latitudes. Together, these findings highlight the distinct yet complementary roles of environmental filtering, human activities, and biotic interactions in shaping freshwater fish invasions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".