Effects of stochastic environmental variation on the population dynamics of salmon lice (Lepeophtheirus salmonis) in Newfoundland and Labrador
Bibliographic record
Abstract
Salmon lice Lepeophtheirus salmonis are a marine parasite causing a significant economic burden in salmonid aquaculture. They experience both temperature-dependent growth and salinity-dependent mortality, impacting population dynamics. Many models have explored the effect of static or seasonal environmental conditions on salmon lice population dynamics, yet none have explored the impact of short-term daily environmental fluctuations. I derived a stochastic population model with daily variability in temperature and salinity, where these fluctuations effect population dynamics through temperature-dependent maturation and salinity-dependent mortality changes. I found that increasing variability in salinity slows population growth rates and decreases the logarithmic abundance of adult females, while increasing daily variability in temperature is a poor indicator of population dynamics, which is better predicted by seasonal temperature trends. Under all stochastic environmental scenarios salmon lice populations persisted and grew in Newfoundland, Canada. Population models are a valuable tool in the management of salmon lice and allow for more sustainable aquaculture practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 | 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".