Evaluating whether metapopulation structure benefits endangered diadromous fishes
Bibliographic record
Abstract
Metapopulation structure is typically thought to increase regional species abundance, promote population persistence, and aid in the re-establishment of extirpated populations. However, the underlying theoretical models tended to assume high productivity, making the conservation benefit of metapopulation structure uncertain for endangered species with low productivity. We simulated population assemblages (N = 50) of diadromous fishes under high to low productivity scenarios to explicitly assess how straying (movement from natal to non-natal rivers) contributes to changes in species abundance and extinction risk. The population aggregation exhibited greater total abundance from source–sink dynamics and also exhibited the rescue effect when productivity remained moderately high. However, straying did not ensure persistence of nonviable populations or enable population re-establishment when productivity was low. These results were robust to a wide range of alternate spatial and life-history parameterizations of the simulation model. Relative to a real-world population aggregation of endangered Atlantic salmon (Salmo salar), our results would argue for a shift in remediation priorities to prevent extinction. Although there is strong evolutionary justification for maintaining widespread distributions of endangered diadromous species, the immediate numerical consequences of this approach may hinder recovery.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".