Of Uberfleas and Krakens: Detecting Trade-offs Using Mixed Models
Bibliographic record
Abstract
SYNOPSIS: All animals experience performance trade-offs as they complete tasks such as capturing prey, defending territories, acquiring mates, and escaping predators. Why then, is it so hard to detect performance trade-offs at the whole-organismal level? Why do we sometimes even obtain positive correlations between two performance traits that are predicted to be negatively associated? Here we explore two plausible explanations. First, most analyses are based on individual maximal values (i.e., personal best), which could introduce a bias in the correlation estimates. Second, phenotypic correlations alone may be poor indicators of a trade-off when contrasting processes occur at the among- versus within-individual levels. One such scenario is the "big houses big cars" model developed in life-history theory to explain the existence of "uberfleas" that are superior in all regards (because they acquire more resources than others). We highlight that the exact opposite scenario might occur for performance trade-offs, where among-individual trade-offs may be masked by within-individual changes in physical condition. One of the best ways to test among these alternative scenarios is to collect repeated pairs of performance traits and analyze them using multivariate mixed models (MMMs). MMMs allow straightforward and simultaneous examination of trait correlations at the among- and within-individual levels. We use a simple simulation tool (SQuID package in R) to create a population of Krakens, a mythical giant squid-like sea creature whose morphology generates a performance trade-off between swimming speed and strength or ability to sink ships. The simulations showed that using individual maximum values introduces a bias that is particularly severe when individuals differ in the number of repeated samples (ntrial). Finally, we show how MMMs can help detect performance (or any other type of) trade-offs and offer additional insights (e.g., help detect plasticity integration). We hope researchers will adopt MMMs when exploring trade-offs in whole-animal performances.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".