The genetic basis of animal behavioural diversity in natural populations
Bibliographic record
Abstract
Individual differences in animal behaviour influence ecological and evolutionary processes. Much behavioural variation has a heritable component, suggesting that genetics may play a role in its development. Yet, the study of the mechanistic description linking genes to behaviour in nature remains in its infancy, and such research is considered a challenge in contemporary biology. Here, we performed a literature review and meta-analysis to assess trends in analytical approaches used to investigate the relationship between genes and behaviour in natural systems, specifically candidate gene approaches, quantitative trait locus (QTL) mapping, and genome-wide association studies (GWAS). We aimed to determine the efficacy and success of each approach, while also describing which behaviours and species were examined by researchers most often. We found that the majority of QTL mapping and GWAS results revealed a significant or suggestive effect (Zr = 0.3 [95% CI: 0.25:0.35] and Zr = 0.39 [0.33:0.46], respectively) between the trait of interest and genetic marker(s) tested, while over half of candidate gene accounts (Zr = 0.16 [0.11:0.21]) did not find a significant association. Approximately a third of all study estimates investigated animal personality traits; though, reproductive and migratory behaviours were also well-represented. Our findings show that despite widespread accessibility of molecular approaches given current sequencing technologies, efforts to elucidate the genetic basis of behaviour in free-ranging systems has been limited to relatively few species. We discuss challenges encountered by researchers, and recommend integration of novel genomic methods with longitudinal studies to usher in the next wave of behavioural genomic research.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".