Learn to hear your prey: The role of associative learning on web building and hunting behaviour in black widow spiders ( <i>Latrodectus hesperus</i> )
Bibliographic record
Abstract
ABSTRACT Individuals from many populations vary consistently in their diet or behaviour. Associative learning could lead to such specialisation by improving the ability of predators to detect or recognize prey types or optimizing the sensory cues or spatial locations that they attend to. In this study, we assess the ability of Western black widow spiders for associative learning in response to vibrational prey cues. Black widow spiders are sedentary predators that detect, identify, and choose whether to attack preys caught in the web using the frequency and strength of vibrations. We conducted two experiments assessing the ability of individuals to associate specific frequencies or spatial locations on the web to the presence of a prey. We analyzed changes in web structure and attack behavior through learning. We hypothesized that individuals would adjust the structure of their webs and their responsiveness towards these frequencies in response to these associations. Spiders did not adjust the structure of their web nor their response to specific web locations when we applied prey items and vibrations at specific locations on the web. Instead, all spiders increased their responsiveness to vibrations at 250 Hz irrespective of their experimental treatment, but not towards 25Hz cues. Unexpectedly, exposing spiders to prey items associated with a 250Hz increased the effect of trap threads on responsiveness itself. Hence, repeated exposures, even when paired with food on rare occasions can alter foraging behavior in this species. Individuals could adjust their foraging behavior by tending more or less intensely to specific sensory information. This generalist predator could change major components of its foraging behavior through learning, but the effect of learning on behavior appears mediated by web structure. Learning combined with variation in web structure could explain the substantial individual differences in attack behavior we observe in this 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) | 0.001 | 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".