Are characteristics of introduced salmonid fishes biased by propagule pressure?
Bibliographic record
Abstract
Many recent studies of nonindigenous species (NIS) have used life history and morphological characteristics of invaders to either (i) build statistical models that predict new invaders or (ii) test ecological and evolutionary hypotheses. However, species characteristics may be confounded if NIS are transported or introduced nonrandomly with respect to the chosen contrast group, which typically consists of native or globally available species. For example, deliberately introduced NIS are often chosen according to economic rather than ecological factors. Here, I use stocking records of salmonid species introduced into Nevada, USA, to test for propagule biases within this system. I find that established salmonids are introduced significantly more times, and in greater numbers, than are those that fail and that species chosen for introduction are a nonrandom sample of the global salmonid species pool. Statistical differences among characteristics of salmonid species that established, those that were introduced, and those from the global source pool suggest that maximum reported size and weight as well as latitudinal range and midlatitude all represent propagule-biased characteristics of salmonids introduced into Nevada. These results highlight the need for caution when using characteristics of invaders to develop statistical models or to test hypotheses relevant to ecology and evolution.
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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 | 0.002 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".