Worms make risky choices too: the effect of starvation on foraging in the common earthworm (<i>Lumbricus terrestris</i>)
Bibliographic record
Abstract
Species should avoid risks to protect accumulated fitness. However, when faced with starvation, organisms may accept risks to enhance future reproductive opportunities. We investigated the effect of starvation on risk-taking behaviour in the common earthworm (Lumbricus terrestris Linnaeus, 1758). Lumbricus terrestris are negatively phototactic annelids that feed on decaying plant matter at the soil surface. Feeding in high-light conditions is a potentially riskier choice, given the threats of visual predators and desiccation. We predicted that starvation in L. terrestris would increase risk-taking behaviour and decrease time taken (latency) to make choices. We manipulated the starvation level of L. terrestris individuals (nonstarved, half-starved, and fully starved) and presented them with a binary foraging choice. Lumbricus terrestris could choose either a low-food and dark condition (low-risk condition) or a high-food and light condition (high-risk condition). We found that starved individuals selected the high-risk condition more often than nonstarved individuals. Starved individuals also had a decreased latency to first choice. Risk-taking did not scale with level of starvation; there was no difference in foraging choice and latency between half- and fully starved individuals. Our results indicate that L. terrestris makes state-dependent foraging choices, providing insight into the importance of fundamental life-history trade-offs in this understudied species.
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) | 0.002 | 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".