Measuring Personality in Wild Small Mammals: A Review of Methods and Proposal for a Standardised Approach
Bibliographic record
Abstract
ABSTRACT Background In recent years, the study of animal personality has gained significant attention in ecology and evolutionary biology. Small mammals are one of the most frequently studied mammalian taxa in this field, and their personality significantly impacts ecological outcomes. However, a review focused on the materials and methods to study wild small mammal personality is lacking. Aims To address this gap, we aim to (1) identify the most consistent assays for measuring specific personality traits in wild species and (2) propose a standardised experimental design, detailing optimal arena size, shape and material, as well as standardised testing conditions and experimental procedures and highlighting critical aspects which require validation. Moreover, we (3) report a clear interpretation of the most commonly measured behavioural traits and the methods employed for their analysis. Material and Methods Our review synthesises findings from 133 articles covering 54 species in a variety of habitats, ranging from the Canadian boreal forests to the semi‐desert regions of South Africa. We found a concerning lack of standardisation in research methodologies, especially for key features such as the shape and size of arenas for behavioural assays and test duration. We observed considerable variability in how behavioural traits were interpreted. Nevertheless, we identified a suite of tests and interpretations of behaviours that allow for efficient processing of animals and produce consistent results in both field and laboratory settings. Conclusion We conclude with five recommendations for a standardised approach to enhance the comparability of results and advance the field of wild small mammal personality 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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".