Environmental effects on the covariation among pace‐of‐life traits
Bibliographic record
Abstract
Abstract Pace‐of‐life syndromes (POLSs) are suites of life‐history, physiological and behavioural traits that arise due to trade‐offs between allocation to current and future reproduction. Traits generally show covariation that can arise from genetic and environmental influences on phenotypes and constrain the independent evolution of traits, resulting in fitness consequences and impacts on population dynamics. The notion that correlations among traits may vary among populations along environmental gradients suggests an important role for the environment in shaping and maintaining POLSs. However, no synthesis has been attempted of the myriad ways in which environmental factors should influence POLSs. Here, we formulate a series of hypotheses targeting the critical interfaces of the environment and life‐history ‐ behaviour associations across different organisms. We discuss the hypotheses in light of findings from a systematic review of studies that measured changes in the association between behaviour and life‐history traits as a function of environmental conditions. The review revealed that POLSs are often shaped by environmental variation, where harshness of the environment in early life has the most consistent effects on POLS. However, only partial or no effects of environmental variation were found in a number of studies, which may result from the highly variable study systems, traits and environments studied. We highlight promising directions arising from the available studies and identify knowledge gaps that, if unaddressed, will impede progress in the field.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".