Family Affects Sibling Cannibalism in the Black Widow Spider,<i>Latrodectus hesperus</i>
Bibliographic record
Abstract
Abstract Adaptive foraging tactics are shaped by genes, the environment and gene–environment interactions. Because of relatively high levels of agonism toward conspecifics, spiders have been a popular focus for behavioral–ecological examinations of conspecific predation, or cannibalism. Surprisingly, studies examining the underlying, proximate assumption that cannibalism in spiders is a heritable trait shaped by interactions between genes and the environment are virtually non‐existent. Here, we examine the influence of family on the expression of sibling cannibalism in the post‐hatching, group‐living phase of an otherwise solitary, web‐building spider, the North American black widow (Latrodectus hesperus). Our results showed significant levels of variation in cannibalistic propensity among 26 sibships, with some families cannibalizing full sibs within 2 d and other families waiting 3 wk before resorting to cannibalism. A similar family‐level effect was evident in measures of sibling cohabitation, voracity toward cricket prey, and development speed. Negative correlations between maternal egg sac investment and offspring cannibalism suggest that this family effect may stem, at least in part, from a maternal effect, although we were not able to directly test the prediction that cannibalism is most common from spiderlings in poor condition. Thus, we present novel data suggesting family effects seem to be responsible for cannibalism inL. hesperusspiderlings; however, future work will be required to disentangle the relative importance of shared genes and shared maternal environment. We discuss several mechanisms that could explain the persistence of family‐level variation in cannibalism, a trait that seems likely to be subject to strong directional selection.
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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 | 0.000 |
| 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".