Evidence for bluegill spawning plasticity obtained by disentangling complex factors related to recruitment
Bibliographic record
Abstract
Fishes can exhibit many forms of plasticity to maximize fitness. However, limited information exists on the ability of freshwater fish to adjust spawning behavior and characteristics (e.g., timing, duration, magnitude of spawning events) to minimize mortality of recruits and ultimately maximize fitness. We wanted to test the life history hypothesis for bluegill (Lepomis macrochirus) (i.e., opportunistic strategy) utilizing existing literature and results from our study to further evaluate the potential for spawning plasticity in this species. Our objective was to identify bluegill recruitment bottlenecks (i.e., periods of high mortality) and factors associated with these events in a single lake during 7 consecutive years. Bluegills exhibited shorter spawning durations and fewer spawning pulses (i.e., peaks in larval production) compared with bluegill in previous studies. Late-hatched (compared with early-hatched) bluegills consistently contributed the most to the fall juvenile population; these recruitment patterns were primarily attributed to biotic drivers. Our study suggests that bluegill could exhibit spawning plasticity and extends our current understanding of adaptations that are potentially capable of increasing fitness for a freshwater fish species under a wide range of environmental conditions and uncertainty.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".