Habitat selection reveals state-dependent foraging trade-offs in a temporally autocorrelated environment
Bibliographic record
Abstract
We use theories of risk allocation to inform trade-offs between foraging in a rich and risky habitat versus using a poor but safe alternative. Recent advances in the theory predict that the length of exposure to good or bad conditions governs risk allocation, and thus habitat choice, when patterns of environmental risk are autocorrelated in time. We investigate the effects of these factors with controlled experiments on a small soil arthropod (Folsomia candida). We subjected animals to nine temporally autocorrelated 16-day feeding treatments varying in both the proportion (0.25, 0.50, and 0.75) and duration (short, medium and long intervals) of time when food was present and absent. We assessed foraging trade-offs by the animals' choice of occupying a risky dry habitat with food (rich) versus a safe moist habitat with no food (poor). Irrespective of autocorrelation in conditions, the proportion of time spent with no food primarily determined habitat selection by these collembolans. Our results imply an energetic threshold below whichF. candidaare forced to forage in rich and risky habitat despite the possibility of mortality through desiccation. The link to energetic thresholds suggests the possibility of employing state-dependent habitat selection as a leading indicator of habitat change.
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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".