Effect of heterospecific and conspecific competition on individual differences in tadpole behaviour
Bibliographic record
Abstract
Abstract Repeated social interactions with conspecifics and/or heterospecifics during early development may drive the differentiation of behaviour among individuals. This behavioural differentiation may occur through individuals behaving more different from each other on average and/or individuals behaving more consistently. Competition is a major form of social interaction and its impacts can depend on whether interactions occur between conspecifics or heterospecifics and the directionality of a response could be specific to different behavioural traits. To test this, we reared tungara frog tadpoles ( Engystomops pustulosus ) either in isolation, with a conspecific tadpole or with an aggressive heterospecific tadpole, the whistling frog tadpole, Leptodactylus fuscus . In each treatment, we measured the body size, activity, exploration and risk taking in the presence of a predator in focal E. pustulosus tadpoles six times during development. We used univariate and multivariate hierarchical mixed effect models to investigate the effect of treatment on mean behaviour and on among individual variance between and within individuals across behavioural traits. There was a strong effect of competition on behaviour, with different population and individual level responses across social treatments. Within their home tank, individuals were more consistent in their movements under conspecific competition but heterospecific competition caused more variance in the average movement among individuals. Behavioural responses were also trait specific as conspecific competition caused greater variability in movements among individuals in a novel environment. The results highlight that the impact of competition on inter-individual differences in behaviour is dependent on competitor species identity and is trait specific.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".