Prey size and scramble vs. contest competition in a social spider: implications for population dynamics
Bibliographic record
Abstract
There are many benefits of group living, but also substantial costs, one of which is competition for resources. How scarce food resources are distributed among different members of a population or social group - whether via scramble or contest competition - can influence not only the variance in individual fitness, but also the stability and therefore survival of the group or population. Attributes of the food resources themselves, such as their size, may influence the type of intraspecific competition that occurs and therefore the intrinsic stability of a group or population. By experimentally manipulating the size of prey fed to artificial colonies of the social spider Anelosimus eximius, we investigated whether prey size could alter the degree of scramble vs. contest competition that takes place and, thus, potentially influence colony population dynamics. We found that large prey were shared more evenly than small prey and that individuals in poor condition were more likely to feed when prey were large than when prey were small. Additionally, we show that individuals participating in prey capture are also more likely to feed on the captured prey. We developed a simple mathematical model to explore the prey sizes that would be energetically worth defending, i.e. prey that are 'economically defendable'. The model shows that neither very small prey, nor prey above a certain size is worth monopolizing, with only intermediate size prey being 'economically defendable'. We therefore suggest the small and large prey in our experiment corresponds to our model's intermediate and large prey categories, respectively. As the size of prey captured by social spider colonies increases with colony size, our findings suggest that scramble competition may predominate in large colonies. Scramble competition, combined with the fact that prey biomass per capita declines as colonies grow beyond a certain size, would then explain why extremely large colonies of this social spider may suddenly go extinct. Our project thus illustrates the potential triple link between characteristics of the resources, individual behaviour and population dynamics, a link rarely considered in an empirical setting.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".