Sea-louse abundance on salmon farms in relation to parasite-control policy and climate change
Bibliographic record
Abstract
Abstract The ectoparasitic copepods, sea lice (Lepeophtheirus salmonis and Caligus spp.), are major pests to salmon aquaculture and can also affect the health and survival of wild salmon. Policies exist to protect wild salmon by delousing farmed fish when louse abundance exceeds a threshold, but their effectiveness under future climate change is uncertain. We fitted a Bayesian model for sea-louse population dynamics and management to timeseries data of sea lice on farmed salmon in Pacific Canada and analysed the model under scenarios of warmer climates. We found that in high-temperature years, current parasite control policy becomes ineffective as sea-louse abundance is expected to increase. We simulated two alternative management scenarios and observed that both would decrease average louse counts on farms in high-temperature years relative to the current system but relied on more delousing treatments than are currently performed. We also found evidence that non-salmonids can play a role in louse transmission to farms, as increased farm colonization of Caligus clemensi occurs in April, coincident with wild herring (Clupea pallasii) spawner abundance. Our results highlight the need for careful management of sea lice on salmon farms in warmer years and the importance of policies designed to account for future environmental change.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".