Habitat conditions during winter explain movement among subpopulations of a declining migratory bird
Bibliographic record
Abstract
BACKGROUND: Understanding animal movement patterns among subpopulations is crucial for identifying spatiotemporal patterns in animal abundance. Quantifying such movement rates enables a better understanding of population dynamics and how animals decide to move between habitats throughout their range. However, detailed assessments of animal movements among subpopulations are often difficult to obtain with typical capture-mark-recapture methods, limiting our ability to incorporate movement information into conservation planning. METHODS: We used six years of high-resolution Global Positioning System (GPS) tracking data in a Bayesian multistate model to quantify habitat drivers of monthly intra- (i.e., from one month to the next within a winter) and inter-winter (i.e., from the last month of one winter to the first month of the following winter) movements among eight different subpopulations of a declining migratory bird, the Greenland white-fronted goose (Anser albifrons flavirostris). RESULTS: We found that while Greenland white-fronted geese were highly philopatric to geographically distinct wintering subpopulations, individuals made intra- and inter-winter movements based on locally available foraging habitat. These decisions changed within and among winters; geese were more likely to make intra-winter movements to areas with fewer croplands and boglands, potentially in response to local food depletion, and more likely to make inter-winter movements to areas with more boglands and “greener” grasslands. CONCLUSIONS: We demonstrated a framework for using high-frequency GPS tracking data to discover movement patterns and test hypotheses about environmental drivers of movements, which could be linked with population dynamics and applied to other species of concern for an improved understanding of metapopulations across space and time. Implementing habitat management strategies that optimize foraging conditions throughout winter, including rewetting degraded peatlands to provide bogland food plants, provision of cereal stubbles in early winter and high-quality grasslands throughout winter may help improve conservation outcomes for Greenland white-fronted geese.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".