The behaviour–performance continuum: how does individual variation in locomotor abilities relate to behaviour?
Bibliographic record
Abstract
A series of terminological, technical, conceptual, and statistical challenges present themselves when trying to study correlations between measures of performance abilities (what an animal can do) and behavioural traits (what an animal chooses to do). We attempt to synthesise literature on this topic, with a specific focus on locomotor performance and behavioural traits measured with standardised tests. We argue that measures of forced performance (e.g. endurance on a motorised treadmill) and voluntary behaviour (e.g. wheel running) often fall along a continuum, sometimes grading into each other. On the performance end of the continuum, tests should measure what an animal can do when motivation is maximal and/or it is given no choice but to exert itself maximally. On the behavioural end of the continuum, tests should capture what animals choose to do of their own free volition, with no experimental attempt to affect motivation. Hence, performance tests attempt to eliminate variation in motivation by forcing all individuals to be maximally motivated, whereas variation in motivation is an inherent component of all behavioural tests. In some cases, however, differentiating between measures of performance versus behaviour can seem almost arbitrary. Moreover, individuals may consistently differ in how willing they are to 'perform' even when 'forced' to do so. We compiled studies reporting any association (covariation, correlation or linear regression) between putative measures of locomotor performance and behaviour in various taxa. The vast majority of those studies report phenotypic correlations, and only a handful have reported genetic correlations or explored potential correlated responses to selection on performance or behaviour. To our knowledge, this is the first global overview of how locomotor performance and behaviour covary in animals, and we believe that our synthesis will be useful to guide future research on locomotor performance and its relationship with other ecologically relevant traits. For example, we argue that a multi-level (co)variance partitioning approach is necessary to gain insights into the importance of how motivation differs across levels (e.g. among- versus within-individual variation, genetic versus environmental variation). Finally, we outline a multitude of compensation and co-specialisation mechanisms that may occur between performance and behaviour, and propose future avenues for research that include selection and manipulative studies to help identify the role of correlational selection, individual experience, and predation detectability on the relationships between behaviour and performance.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".