Effect of Nesting Habitat on Niche Width and Reproductive Success of Peregrine Falcons Nesting Near Rankin Inlet, Nunavut.
Bibliographic record
Abstract
Although variability in resource use is well known in wild animals, proximate causes are difficult to identify, and fitness consequences are poorly known.We investigated niche variation in a population of Peregrine Falcons (Falco peregrinus tundrius) inhabiting a coastal environment around Rankin Inlet, Nunavut.We evaluated whether nesting habitat influenced niche variation and breeding success in falcons nesting in three habitat types (inland, coastal, island).We tested two competing hypotheses.The "restricted generalist hypothesis" assumes that Peregrine Falcons have limited ability to exploit marine resources and thus predicts that terrestrial resources would form the bulk of their diet regardless of nesting habitat.As restricted generalists, Peregrines nesting in marine dominated landscapes are predicted to fledge fewer young given the additional costs associated with foraging and handling time (lower provisioning rates).On the other hand, the "flexible generalist hypothesis" assumes that Peregrines are not limited in their ability to exploit marine prey.Individuals nesting in marine-dominated landscapes would thus use marine subsidies and show a shift in diet characterized by increased proportions of marine prey.As "flexible generalists," provisioning rate and reproductive success are predicted to be similar regardless of nesting habitat type.
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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".