Behavior and Physiology Outpace Form When Linking Traits to Ecological Responses Within Populations: A Meta-Analysis
Bibliographic record
Abstract
Abstract Intraspecific variability is fundamental to ecology, yet we still know remarkably little about what governs the strength of the associations between traits expressed by individuals and ecological dynamics. To explore this overlooked aspect of diversity, we asked whether the strength of correlations between traits and a wide spectrum of ecological responses could differ ( i ) between intraspecific levels (among vs . within populations), ( ii ) among ecological responses across levels of biological organization (from ecological performance to ecosystem functioning), and ( iii ) among trait types (morphology, physiology, and behavior). We performed a meta-analysis synthesizing over a thousand effect sizes from nearly two hundred studies spanning approximately a hundred animal species across a broad range of traits and ecological responses. The average effect size was | r | = 0.26 (95% confidence interval: 0.21 – 0.30). At the individual level, effect sizes were larger for ecological performance (foraging, diet) than for fitness (reproduction), and tended to be larger for community responses (e.g., community composition of surrounding organisms). Physiology and behavior showed larger effect sizes than morphology. Our meta-analysis not only confirms that intraspecific trait variability is central to ecological dynamics, but also highlights physiology and behavior as key traits for unraveling the ecological consequences of individual variability.
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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 | 0.027 | 0.035 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.036 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".