Spatial scale in games of habitat selection, patch use, and sympatric speciation
Bibliographic record
Abstract
Most organisms live in heterogeneous environments. Yet we know little about how variations in scales of heterogeneity influence decisions on patch use and habitat selection, and how they impact spatial distribution and evolution. In particular, we need to know whether the choice of habitats and patches emerges from a hierarchy of decisions, whether resource consumption correlates closely with space use, and whether different types of individuals are associated with patterns of spatial distribution. I address these knowledge gaps with field experiments that manipulated the risk and quality of foraging patches exploited by male meadow voles. I used clear versus wooden covers to create risky versus safe foraging sites and added supplemental food to create rich versus poor habitats. I assessed whether the resources harvested from each tray matched its frequency of use by groups of voles expressing different temperament scores. Habitat and patch use did not fit a simple hierarchy of decisions because animals merged space use and foraging speed in a sophisticated strategy of risk management. Giving-up densities mirrored activity densities at the scale of safe versus risky patches but not at the scale of safe versus risky or rich versus poor habitats. Voles tended to prefer one habitat over another for reasons independent of the experimental manipulations. Groups of voles with different temperament scores were not linked to foraging types but were linked to habitat preference. The bias in habitat use by different behavioural types provides a potential mechanism for the evolutionary divergence of populations occupying different habitats.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".