Risky mate search and mate preference in the golden orb-web spider (Nephila plumipes)
Bibliographic record
Abstract
Mate searching is a risky behavior that decreases survival by increasing predation risk and the risk of energy depletion. However, few studies have quantified actual mortality during mate search, making it difficult to predict mate searching and mating strategies. Using a mark and recapture study, we examined mate-searching success in a highly sexually dimorphic species, the golden orb-web spider (Nephila plumipes). We show that despite the high-density aggregations of this species, male survival during mate searching is extremely low (36%) and is phenotype independent. Surprisingly, males that survived mate search were in better condition after recapture than prior to release, most likely due to kleptoparasitism on females' webs. In a complementary release experiment in a field enclosure, we show that males are choosy and adjust their choice of female depending on their own condition and weight. Thus, the high mortality rate of searching males in the field may be a cost of choosiness because released males traveled further than necessary to settle on females. Although males were choosy about female phenotypes, they did not avoid webs with rival males already present. This suggests that the cost of continued searching outweighs the cost of competition but not the cost of mating with certain females. Further examinations of mate-searching risk in other species in reference to their mating system and environmental conditions are necessary to determine the occurrence and effects of high mortality rates during searching.
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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".