Peregrine falcons shift mean and variance in provisioning in response to increasing brood demand
Bibliographic record
Abstract
Abstract The hierarchical model of provisioning posits that parents employ a strategic, sequential use of three provisioning tactics as offspring demand increases (e.g., due to increasing brood size and age). Namely, increasing delivery rate (reducing intervals between provisioning visits), expanding provisioned diet breadth, and adopting variance-sensitive provisioning. We evaluated this model in an Arctic breeding population of Peregrine falcons (Falco peregrinus tundrius) by analyzing changes in inter-visit-intervals (IVIs) and residual variance in IVIs across 7 study years. Data were collected using motion-sensitive nest camera images and analyzed using Bayesian mixed effect models. We found strong support for a decrease in IVIs (i.e., increase in delivery rates) between provisioning visits and an increase in residual variance in IVIs with increasing nestling age, consistent with the notion that peregrines shift to variance-prone provisioning strategies with increasing nestling demand. However, support for predictions made based on the hierarchical model of tactics for coping with increased brood demand was equivocal as we did not find evidence in support of expected covariances between random effects (i.e., between IVI to an average sized brood (intercept), change in IVI with brood demand (slope) or variance in IVI). Overall, our study provides important biological insights into how parents cope with increased brood demand.
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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 | 0.000 | 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".