Local recruitment in Northern Flickers is related to environmental factors at multiple scales and provides reproductive benefits to yearling breeders settling close to home
Bibliographic record
Abstract
Natal dispersal and local recruitment are affected by factors both intrinsic and extrinsic to juveniles and may affect fitness. Understanding the relationship between dispersal and population density in birds has been hindered by a lack of long-term studies and a focus on resident species has neglected the role of weather operating at large spatial scales. I studied local recruitment and the reproductive consequences of natal dispersal distance within a population of Northern Flickers (Colaptes auratus), a migratory woodpecker. During a field study spanning 16 years in British Columbia, 8272 fledglings were banded and 138 males and 105 females recruited locally. Average annual local recruitment rate for males (3.36%) was greater than for females (2.55%) and propensity to recruit locally was positively correlated with an early hatch date and high body condition. Annual local recruitment was not associated with population density in the year of hatch but was positively correlated with population density in the year of settlement. Local recruitment was also positively correlated with warmer springs during migration consistent with the phenology hypothesis. Among local recruits, natal dispersal distance was independent of the presence of parents and so dispersal was not a behavior to prevent inbreeding. However, settling closer to the natal site led to reproductive benefits in terms of earlier laying dates and better nest success. Therefore, juveniles may gain useful information about the location of nesting substrates, predation risk, and patchy food resources by exploring the landscape around their natal site during the post-fledging period and then settling in the familiar area after returning from migration.
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 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".