LIFE-HISTORY EVOLUTION IN GUPPIES VIII: THE DEMOGRAPHICS OF DENSITY REGULATION IN GUPPIES (POECILIA RETICULATA)
Bibliographic record
Abstract
In prior research, we found the way guppy life histories evolve in response to living in environments with a high or low risk of predation is consistent with life-history theory that assumes no density dependence. We later found that guppies from high-predation environments experience higher mortality rates than those from low-predation environments, but the increased risk was evenly distributed across all age/size classes. Life-history theory that assumes density-independent population growth predicts that life histories will not evolve under such circumstances, yet we have shown with field introduction experiments that they do evolve. However, theory that incorporates density regulation predicts this pattern of mortality can result in the patterns of life-history evolution we had observed. Here we report on density manipulation experiments performed in populations of guppies from low-predation environments to ask whether natural populations normally experience density regulation and, if so, to characterize the short-term demographic changes that underlie density regulation. Our experiments reveal that these populations are density regulated. Decreased density resulted in higher juvenile growth, decreased juvenile mortality rates, and increased reproductive investment by adult females. Increased density causes reduced offspring size, decreased fat storage by adult females, and increased adult mortality.
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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 | 0.000 |
| 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 | 0.000 | 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".