Importance of body size and hunting strategy during interactions between the Mexican red-rump tarantula (<i>Brachypelma</i><i>vagans</i>) and the wolf spider<i>Lycosa</i><i>subfusca</i>
Bibliographic record
Abstract
Behavioural adaptation helps animals to maximize their ability to obtain food and to avoid being eaten, increasing fitness. To achieve this, they must assess predation risk and evaluate foraging needs simultaneously. In two sympatric spider species, the wandering wolf spider Lycosa subfusca F.O.P. Cambridge, 1902 and the sit-and-wait Mexican red-rump tarantula (Brachypelma vagans Ausserer, 1875), we studied the relationship between predatory behaviour and antipredatory behaviour at different life stages. In the laboratory, encounters were organized between one wolf spider (small, medium-sized, or large) and one tarantula (spiderling, small, medium-sized, or large). Attack latencies and behaviours were recorded. The results showed that wolf spiders attacked and successfully captured younger tarantulas, while they avoided or retreated from older ones. Tarantulas preferentially attacked and captured older wolf spiders. On other hand, younger wolf spiders were more cautious than older ones, which waited until for the tarantulas to attack before retreating. Younger tarantulas were also more cautious than adults, which never retreated from attack and increased their success in attacks with age. Finally, we discuss the relationship between the predatory strategies of both spiders with their perception abilities and life history.
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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) | 0.000 | 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".