Behavioral Syndromes: An Integrative Overview
Bibliographic record
Abstract
A behavioral syndrome is a suite of correlated behaviors expressed either within a given behavioral context (e.g., correlations between foraging behaviors in different habitats) or across different contexts (e.g., correlations among feeding, antipredator, mating, aggressive, and dispersal behaviors). For example, some individuals (and genotypes) might be generally more aggressive, more active or bold, while others are generally less aggressive, active or bold. This phenomenon has been studied in detail in humans, some primates, laboratory rodents, and some domesticated animals, but has rarely been studied in other organisms, and rarely examined from an evolutionary or ecological perspective. Here, we present an integrative overview on the potential importance of behavioral syndromes in evolution and ecology. A central idea is that behavioral correlations generate tradeoffs; for example, an aggressive genotype might do well in situations where high aggression is favored, but might be inappropriately aggressive in situations where low aggression is favored (and vice versa for a low aggression genotype). Behavioral syndromes can thereby result in maladaptive behavior in some contexts, and potentially maintain individual variation in behavior in a variable environment. We suggest terminology and methods for studying behavioral syndromes, review examples, discuss evolutionary and proximate approaches for understanding behavioral syndromes, note insights from human personality research, and outline some potentially important ecological implications. Overall, we suggest that behavioral syndromes could play a useful role as an integrative bridge between genetics, experience, neuroendocrine mechanisms, evolution, and ecology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".