Examining environmental and life history correlates of winter and migratory movements in a nomadic Arctic-breeding passerine
Bibliographic record
Abstract
The Snow Bunting (Plectrophenax nivalis) is an Arctic-breeding songbird facing population declines. The cause for these declines, however, is not well understood because of the difficulty of studying this species due to their remote breeding grounds in the Arctic and nomadic wintering movement. With climate change increasing ambient temperatures and affecting weather systems at a global scale, Snow Buntings will be affected at all life stages. To help fill in knowledge gaps for the wintering stage of this species, the goal of this research was to understand patterns of movements of Snow Buntings overwintering in Ontario, Canada. Specifically, we first investigated which environmental and intrinsic factors influenced space use during the wintering period, and used this as a basis to estimate spring migration initiation. To accomplish these goals, we spatially tracked birds using automated digitally-coded radiotelemetry (Motus Wildlife Tracking System) tags deployed on wintering birds in Southwestern Ontario. Additionally, while some individuals were banded, tagged, and released where they were caught, others were strategically displaced by several hundred kilometers to causally test their response to different environmental conditions. We found that surface pressure was an important extrinsic factor influencing movement decisions in wintering buntings. Further, surface pressure, precipitable water in the atmosphere, and wind speeds were important predictors of whether detections were associated with differentiating wintering or migration movements. However, we found no evidence to support the role of sex or body size influencing movement decisions in wintering or early-migration buntings. Finally, combining tracking and band-return data, we found support for a previously undescribed spring migratory pathway. Quantifying the movement ecology of Snow Buntings in Southwestern Ontario will help to determine whether they have the capacity to adapt to an increasingly changing landscape across multiple life stages.
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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 | 0.001 | 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".