Estimating marine survival of Atlantic salmon using an inverse matrix approach
Bibliographic record
Abstract
The marine phase of anadromous Atlantic salmon (Salmo salar) is the least known yet one of the most crucial with regards to population persistence. Recently, declines in many salmon populations in eastern Canada have been attributed to changes in the conditions at sea, thus reducing their survival. However, marine survival estimates are difficult to obtain given that many individuals spend multiple winters in the ocean before returning to freshwater to spawn; therefore, multiple parameters need to be estimated. We develop a model that uses an age-structured projection matrix which, coupled with yearly smolt and return abundance estimates, allows us to resample a distribution of matrices weighted by how close the resulting return estimates match the simulated returns, using a sample-importance-resampling algorithm. We test this model by simulating a simple time series of salmon abundances, and generate six different scenarios of varying salmon life histories where we simulate data for one-sea-winter (1SW)-dominated and non-1SW dominated populations, as well as scenarios where the proportion returning as 1SW is stable or highly variable. We find that our model provides reasonable estimates of marine survival for the first year at sea (S1), but highly uncertain estimates of proportion returning as 1SW (Pr) and survival in the second year at sea (S2). Our exploration of variable scenarios suggests the model is able to detect temporal trends in S1 for populations that have a considerable 1SW component in the returns; the ability of the model to detect trends in S1 diminishes as the proportion of two-sea-winter fish increases. Variability in the annual proportion of fish returning as 1SW does not seem to impact model accuracy. Our approach provides an instructive stepping-stone towards a model that can be applied to empirical abundance estimates of Atlantic salmon, and anadromous fishes in general, and therefore improve our knowledge of the marine phase of their life cycles as well as examining spatial and temporal trends in their variability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".