Annual variation in habitat-specific recruitment success: implications from an individual-based model of Lake Michigan alewife (Alosa pseudoharengus)
Bibliographic record
Abstract
The identification of important spawning and nursery habitats for fish stocks can aid fisheries management, but is complicated by various factors, including annual variation in recruitment success. The alewife ( Alosa pseudoharengus ) is an ecologically important species in Lake Michigan that utilizes a variety of habitats for spawning and early life growth. While productive, warm tributary mouths (connected to Lake Michigan) may contribute disproportionately more recruits (relative to their habitat volume) to the adult alewife population than cooler, less productive nearshore habitats, the extent of interannual variation in the relative contributions of recruits from these two habitat types remains unknown. We used an individual-based bioenergetics simulation model and input data on daily temperatures to estimate alewife recruitment to the adult population by these different habitat types. Simulations suggest that nearshore lake habitats typically produce the vast majority of young alewife recruits. However, tributary habitats may contribute the majority of alewife recruits during years of low recruitment. We suggest that high interannual variation in the relative importance of habitats for recruitment is a common phenomenon, which should be considered when developing habitat management plans for fish populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".