Personalities influence spatial responses to environmental fluctuations in wild fish
Bibliographic record
Abstract
Although growing evidence supports the idea that animal personality can explain plasticity in response to changes in the social environment, it remains to be tested whether it can explain spatial responses of individuals in the face of natural environmental fluctuations. This is a major challenge in ecology and evolution as spatial dynamics link individual- and population-level processes. In this study, we investigated the potential of individual personalities to predict differences in fish behaviour in the wild. Specifically, our goal was to answer if individual differences in plasticity of space use to sea surface temperature could be explained by differences in personality along the reactive-proactive axis. To address this question, we first conducted repeated standard laboratory assays (i.e., open-field test, novel object test and mirror stimulation test) to assess the personality type of 76 wild-caught Atlantic cod (Gadus morhua). Next, we released the fish back into the sea and monitored their spatial behaviour over large temporal (16 months) and spatial (a whole fjord) scales, using high-resolution acoustic tracking. We demonstrate that (a) cod personality traits are structured into a proactive-reactive syndrome (proactive fish being more bold, exploratory and aggressive), (b) mean depth use of individuals is mainly driven by sea temperature and (c) personality is a significant predictor of home range changes in the wild, where reactive, but not proactive, individuals reduced their home range as sea temperature increased. These findings expand our understanding of the ecological consequences of animal personality and the mechanisms shaping spatial dynamics of animals in nature.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".