Landscape heterogeneity drives intra‐population niche variation and reproduction in an arctic top predator
Bibliographic record
Abstract
While intra-population variability in resource use is ubiquitous, little is known of how this measure of niche diversity varies in space and its role in population dynamics. Here we examined how heterogeneous breeding environments can structure intra-population niche variation in both resource use and reproductive output. We investigated intra-population niche variation in the Arctic tundra ecosystem, studying peregrine falcon (Falco peregrinus tundrius, White) breeding within a terrestrial-marine gradient near Rankin Inlet, Nunavut, Canada. Using stable isotope analysis, we found that intra-population niches varied at the individual level; we examined within-nest and among-nest variation, though only the latter varied along the terrestrial-marine gradient (i.e., increased among-nest variability among birds nesting within the marine environment, indicating higher degree of specialization). Terrestrial prey species (small herbivores and insectivores) were consumed by virtually all falcons. Falcons nesting within the marine environment made use of marine prey (sea birds), but depended heavily on terrestrial prey (up to 90% of the diet). Using 28-years of peregrine falcon nesting data, we found a positive relationship between the proportion of terrestrial habitat surrounding nest sites and annual nestling production, but no relationship with the likelihood of successfully rearing at least one nestling reaching 25 days old. Annually, successful inland breeders raised 0.47 more young on average compared to offshore breeders, which yields potential fitness consequences for this long-living species. The analyses of niche and reproductive success suggest a potential breeding cost for accessing distant terrestrial prey, perhaps due to additional traveling costs, for those individuals with marine nest site locations. Our study indicates how landscape heterogeneity can generate proximate (niche variation) and ultimate (reproduction) consequences on a population of generalist predator. We also show that within-individual and among-individual variation are not mutually exclusive, but can simultaneously arise and structure intra-population niche variation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".