The pace and sequence of spatial learning: exploration facilitates long‐term behavioral refinement
Bibliographic record
Abstract
Understanding how animals learn in novel environments is crucial for predicting behavioral responses to rapid environmental change, yet we lack knowledge about how long different behaviors take to develop and refine, and how exploration facilitates learning. We tested the exploration–refinement hypothesis using movement and diet data from GPS‐collared bison Bison bison (n = 10) monitored for five years following reintroduction to Banff National Park. We examined how exploration influenced movement efficiency, habitat selection behaviors, and home range establishment. Different behaviors showed distinct learning trajectories. Movement efficiency in familiar areas reached an inflection point at 384 days, but when facing unfamiliar terrain, efficiency decreased as animals needed time to learn the new area. Habitat selection behaviors showed rapid initial improvement followed by extended refinement periods lasting up to three years. Exploratory movements occurred primarily in the first year (57%) but continued throughout the study and were positively correlated with improvements in habitat selection behaviors and home range stabilization, but not with movement efficiency. Our results demonstrate that exploration facilitates spatial learning – the process of acquiring and using information about environmental structure, distances, and relative positions to navigate effectively, locate resources, and avoid threats. This suggests that allowing individuals to explore while preventing large dispersal movements may be required for successful reintroductions. The extended timeline required for spatial behaviors to stabilize (3–4 years) indicates that behavioral refinement takes substantially longer than typically assumed, highlighting the need to align monitoring periods with the biological timeframes required for animals to learn novel environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".