Causes and consequences of dispersal in a resident boreal passerine
Bibliographic record
Abstract
Knowledge of the mechanisms governing, and the downstream effects of dispersal movements is essential to understanding the implications of dispersal for individual fitness, gene flow, and population dynamics. In this thesis, I integrate radio-tracking and long-term demographic data to investigate the causes and consequences of natal and breeding dispersal in a food-caching passerine, the Canada jay (Perisoreus canadensis), in Algonquin Provincial Park, Ontario, Canada. In my first chapter, I review the literature to synthesize what is known about the causes and consequences of natal and breeding dispersal in birds. In my second chapter, I use radio-tracking data to examine the patterns of juvenile dispersal and survival in first-year Canada jays to demonstrate the survival consequences of delayed dispersal. In my third chapter, I combine radio-tracking and long-term demographic data to assess the effects of early-life social status and juvenile dispersal on direct and inclusive fitness. In my fourth chapter, I use long-term breeding dispersal data on Canada jays to examine the factors influencing adults to switch breeding territories between years. In my final chapter, I use long-term breeding dispersal and demographic data to investigate the short- and long-term fitness consequences of breeding dispersal in adults. Together, my thesis highlights how dispersal can carry-over to influence individual fitness, but also demonstrates that the causes and consequences of dispersal vary between juveniles and adults and according to social and environmental conditions.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".