Nest- and territory-scale predictors of nest-site selection, and reproductive investment and success in a northern population of American kestrels (<i>Falco sparverius</i>)
Bibliographic record
Abstract
In a heterogeneous landscape, birds must evaluate environmental cues that signal the fitness benefits to be gained from a breeding site. Little study has been devoted to the factors that influence settlement decisions and their implications for breeding in northern populations of American kestrels. We examined nest-site selection and reproductive investment and success of this species in relation to the abundance of small mammals from 1990 to 1997 and territory and nest-site attributes in 2008. Nest-site selection was not associated with prey abundance; however, females initiated laying earlier on territories with higher prey abundance. Kestrels were more likely to choose nest boxes with unobstructed entrances and in recently harvested forests and laid clutches of lower volume in forests with a heavier deciduous component. Nestling mass (females) was greater in boxes at the forest edge and on jack pine, and feather lengths (males) were greater in nests on trees in poor health. We discuss the importance of these features for provisioning and nest vigilance and propose that kestrels in our area make decisions based on interactions occurring at scales intermediate to the landscape and territory levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".