Spatial–social familiarity complements the spatial–social interface: evidence from Yellowstone bison
Bibliographic record
Abstract
Social animals make behavioural decisions based on local habitat and conspecifics, as well as memorized past experience (i.e. ‘familiarity’) with habitat and conspecifics. Here, we develop a conceptual and empirical understanding of how spatial and social familiarity fit within the spatial–social interface—a novel framework integrating the spatial and social components of animal behaviour. We conducted a multi-scale analysis of the movements of GPS-collared plains bison ( Bison bison , n = 66) residing in and around Yellowstone National Park, USA. We found that both spatial and social familiarity mediate how individuals respond to their spatial and social environments. For instance, individuals with high spatial familiarity rely on their own knowledge as opposed to their conspecifics, and individuals with high social familiarity rely more strongly on the movement of conspecifics to guide their own movement. We also found that fine-scale spatial and social phenotypes often scale up to broad-scale phenotypes. For instance, bison that select more strongly to align with their nearest neighbour have larger home ranges. By integrating spatial and social familiarity into the spatial–social interface, we demonstrate the utility of the interface for testing hypotheses, while also highlighting the pervasive importance of cognitive mechanisms in animal behaviour. This article is part of the theme issue ‘The spatial–social interface: a theoretical and empirical integration’.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".