An experimental test of the ecological mechanisms driving density-mediated carry-over effects in a seasonal population
Bibliographic record
Abstract
Carry-over effects occur when past experience influences current individual performance. Although variation in conspecific density in one season has been shown to carry over to influence dynamics in the following season, the proximate ecological mechanisms driving these effects are unknown. One hypothesis is that high density decreases food availability, resulting in poor physiological condition, which in turn compromises performance the next season. Alternatively, high conspecific density could also lead to a high degree of antagonistic interactions, decreasing the amount of time individuals spend foraging. To investigate these hypotheses, we applied a factorial design where both conspecific density and per capita food availability during the non-breeding period were independently manipulated in seasonal populations of common fruit flies (Drosophila melanogaster Meigen, 1830). Individual condition at the beginning of the breeding period was influenced by per capita food availability but not density during the previous non-breeding period. In contrast, reproductive output was most strongly influenced by the interaction between per capita food availability and density in the previous non-breeding period, such that populations that experienced high non-breeding densities and low food availability had the lowest reproductive output. However, the strength of this effect was relatively weak. Our results demonstrate how environmental and social conditions in one part of the annual cycle can carry over to influence individual performance in subsequent periods.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".