Climate change, phenology, and habitat degradation: drivers of gosling body condition and juvenile survival in lesser snow geese
Bibliographic record
Abstract
Nesting migratory geese are among the dominant herbivores in (sub) arctic environments, which have undergone unprecedented increases in temperatures and plant growing days over the last three decades. Within these regions, the Hudson Bay Lowlands are home to an overabundant breeding population of lesser snow geese that has dramatically damaged the ecosystem, with cascading effects at multiple trophic levels. In some areas the overabundance of geese has led to a drastic reduction in available forage. In addition, warming of this region has widened the gap between goose migration timing and plant green-up, and this 'mismatch' between goose and plant phenologies could in turn affect gosling development. The dual effects of climate change and habitat quality on gosling body condition and juvenile survival are not known, but are critical for predicting population growth and related degradation of (sub) arctic ecosystems. To address these issues, we used information on female goslings marked and measured between 1978 and 2005 (4125 individuals). Goslings that developed within and near the traditional center of the breeding colony experienced the effects of long-term habitat degradation: body condition and juvenile survival declined over time. In newly colonized areas, however, we observed the opposite pattern (increase in body condition and juvenile survival). In addition, warmer than average winters and summers resulted in lower gosling body condition and first-year survival. Too few plant 'growing days' in the spring relative to hatch led to similar results. Our assessment indicates that geese are recovering from habitat degradation by moving to newly colonized locales. However, a warmer climate could negatively affect snow goose populations in the long-run, but it will depend on which seasons warm the fastest. These antagonistic mechanisms will require further study to help predict snow goose population dynamics and manage the trophic cascade they induce.
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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) | 0.000 | 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".