Quasi-cycles in crappie populations are forced by interactions among population characteristics and environment
Bibliographic record
Abstract
Crappie (Pomoxis spp.) populations have been characterized as cyclic, with strong year-classes recurring at 2- to 4-year intervals. We evaluated the potential for cyclic trends in crappie populations using a population model that included a density-dependent stock recruitment function and random environmental variation. Slow, medium, and fast growth were simulated over 100 years. The model predicted highly variable recruitment that was strongly influenced by environmental fluctuation at low and intermediate stock densities. At high stock density, recruitment was low, even if environmental conditions were favorable. Significant quasi-cycles occurred, but they were not sustained throughout the time series due to random environmental fluctuation. Quasi-cycles occurred because intermediate stock density and favorable environmental conditions occasionally combined to produce a very strong year-class that greatly increased stock density in the following 13 years and produced low recruitment, even if environmental conditions were favorable. Empirical data from 32 years of sampling age-0 crappies at Ross Barnett Reservoir showed trends similar to the simulated fluctuations. We conclude that crappie populations likely do not exhibit true cycles but may show quasi-cycles as a result of the interaction between random fluctuations in environment and density-dependent mechanisms. The frequency of such quasi-cycles may be enhanced by rapid growth and high exploitation.
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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 | 0.004 |
| 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 | 0.001 |
| 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".