A standardized approach to estimate life history tradeoffs in evolutionary ecology
Bibliographic record
Abstract
As tradeoffs limit the maximum Darwinian fitness individuals can reach, measuring reliably the strength of tradeoffs using appropriate metrics is of prime importance to understand the evolution of traits under constraints. Tradeoffs involving phenotypic traits and fitness components, however, are difficult to quantify in free‐ranging populations because of confounding effects due to environmental variation and individual heterogeneity. Furthermore, although some methods have been used previously to quantify tradeoffs, these methods cannot be applied with respect to binary traits, which are common to describe life histories (e.g. probability of reproduction, nesting success, offspring survival). Here, we demonstrate how to measure reliably the strength of tradeoffs involving binary traits using (auto)correlation estimates obtained from generalized linear (mixed) models. We first propose a standardized approach that accounts for the variation in the nature of the tradeoffs being compared (e.g. continuous/binary traits, repeated/non‐repeated measures), and then apply this method to longitudinal data from two contrasting species of large herbivores. Empirical estimates of tradeoffs varied among traits, and between‐species comparisons suggested that reproductive tradeoffs between successive breeding attempts might only occur in capital breeders. The empirical results we obtained clearly demonstrate that the method we provide allows measuring reliably the strength of tradeoffs under most circumstances, including tradeoffs on binary traits. Our original approach therefore offers an important first step for comparing the strength and, hence, the relative importance of different tradeoffs, and opens the door to a better understanding of the evolution of life history traits in free‐ranging populations.
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".