Larval Crowding Effects on Drosophila Melanogaster populations selected for Early and late life fertility
Bibliographic record
Abstract
Life history theory seeks to explain how organisms optimize their fitness in the presence of extrinsic and intrinsic challenges. Like many organisms with complex life cycles, holometabolous insects often face different environments and uncertain future conditions as they differentiate and grow. These extrinsic factors interact with tradeoffs within stages, such as the rate of growth and size at each transition point. Here, I study the effects of larval crowding on populations selected for rapid development and extreme early life fertility (FAST) and relaxed development rate and late life fertility (SLOW) during larval and pupal (pre-adult), and adult stages of Drosophila melanogaster. I do so by quantifying pre-adult developmental time and viability, as well as adult size at eclosion and reproductive success. Additionally, I tease apart sex differences in terms of sex ratio at eclosion and weight changes with increased larval crowding. I found the following: larval development time increased for both selection regimes, but this change was more pronounced in the SLOW selected populations. Additionally, the SLOW selected populations exhibited prolonged pupal development at high larval densities of 200 and 400 per vial. The most significant decrease in pre-adult viability was predominantly during larval development for both selection regimes. Size at eclosion decreases as larval density increases, and is closely correlated with a decrease in fecundity. Yet, there were few differences in the response to crowding between the males and females for both selection regimes in terms of eclosion time or weight loss due to density. My results support other studies on the effects of larval crowding, as well as offering new insights into the physiological plasticity and trade-offs that the two selection regimes exhibit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".