Foraging actively can be advantageous in heterogeneous environments
Bibliographic record
Abstract
A wealth of evidence indicates that behavioural polymorphism is widespread in nature. While some organisms search for food by moving almost continuously throughout their environment, other organisms forage in one place for long periods of time. Although such a dichotomy has been previously documented in Drosophila melanogaster , the question remains which foraging strategy is better suited to maximize energy intake in a particular environment. We designed an experiment to evaluate whether the configuration of food in the environment alters the foraging behaviour of two larval strains. Assuming that one of the strains acquires more food than the other in a given environment, we examined whether variation in growth occurred between them. Our results indicate that foraging behaviour is a plastic trait, shaped by the configuration of food in the environment. Regardless of the foraging strategy, we found that larvae generally increased their locomotion when food was patchy rather than clumped. Even though we observed that some individuals actively sought food while others stayed foraging at nearby sites, we found no differences in growth rate between them. However, we suggest that foraging actively may be advantageous in polymorphic populations because such behaviour facilitates local adaptation via founder effect and gene flow.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".