Environmental programming of adult foraging behavior in <i>C. elegans</i>
Bibliographic record
Abstract
Abstract Foraging strategies must be tuned to the availability and distribution of resources in the environment. This can occur over generations and lead to genetic differences in foraging behavior, or it can occur on shorter time scales within an individual’s life span. Both genetic and experience-based strategies must be implemented by neural circuits that respond to environmental cues and track internal states, and the analysis of such circuits provides insight into the neural basis of complex decision making. In C. elegans, between-strain genetic differences and within-strain plasticity in foraging has been observed. Most individual changes in foraging are short-term, based on experience over several hours. Here, we tested if developmental experience could permanently alter foraging. We found that in wild strains that are normally highly exploratory, early-life starvation leads to “cautious” foraging behavior in which exploration is reduced. We characterize the behavioral bases for these strategies and identify changes in the dynamics of a locomotory circuit involved in navigation. Overall, we show that some C. elegans strains exhibit adaptive tuning of their foraging behavior based on early-life experience, and this is associated with changes in a core navigation circuit.
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) | 0.001 | 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".