Invading and range-expanding pink salmon inform management actions for marine species on the move
Bibliographic record
Abstract
Abstract Species redistributions, whether resulting from invasions or range expansions, pose similar challenges for conservation and management. Redistribution impacts on host ecosystems and species combine with those from climate change, which are already acute at northern latitudes. Using pink salmon Oncorhynchus gorbuscha, which are native to the Pacific Ocean, we employ knowledge exchange to inform decision-making in non-native marine areas: they are expanding their range to the Arctic Ocean and are invasive in the Atlantic Ocean. The predicted future marine distribution of pink salmon focuses effort on where and when pink salmon are present and informs on potential interactions with native species. Management actions taken in the Atlantic Ocean to reduce invasive pink salmon are resource-intensive, but removed salmon could be a food resource. Addressing identified gaps regarding the invasion potential of pink salmon, interactions among pink salmon and other species, and current mitigation efforts would support forward-thinking management decisions given predictions of continued environmental change. We also highlight steps that can be taken immediately to coordinate actions and better inform responses. Managed for production in the Pacific and as an invasion in the Atlantic, pink salmon provide a tangible approach to informed decision-making through collaboration for marine species on the move.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".