Geometry of the ideal free distribution: individual behavioural variation and annual reproductive success in aggregations of a social ungulate
Bibliographic record
Abstract
Variation in social environment can mitigate risks and rewards associated with occupying a particular patch. We aim to integrate Ideal Free Distribution (IFD) and Geometry of the Selfish Herd (GSH) to address an apparent conflict in their predictions of equal mean fitness between patches (IFD) and declining fitness benefits within a patch (GSH). We tested these hypotheses in a socio-spatial context using individual caribou that were aggregated or disaggregated during calving and varied in their annual reproductive success (ARS). We then tested individual consistency of these spatial tactics. We reveal that two socio-spatial tactics accorded similar mean ARS (IFD); however, ARS for aggregated individuals declined near the periphery (GSH). Individuals near the aggregation periphery exhibited flexibility, whereas others were consistent. The integration of classical theories through a contemporary lens of consistent individual differences provides evidence for an integrated GSH and IFD strategy that may represent an evolutionary stable state.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".