Nest traits and major flooding events influence nest survival of Emperor Geese while regional environmental variation linked to climate does not
Bibliographic record
Abstract
Abstract The reproductive ecology of geese that breed in the Arctic and subarctic is likely susceptible to the effects of climate change, which is projected to alter the environmental conditions of northern latitudes. Nest survival is an important component of productivity in geese; however, the effects of regional environmental conditions on nest survival are not well understood for some species, including the Emperor Goose (Anser canagicus), a species of conservation concern that is endemic to the Bering Sea region. We estimated nest survival and examined how indices of regional environmental conditions, nest traits (nest age, initiation date, and maximum number of eggs in the nest), and researcher disturbance influenced daily survival probability (DSP) of Emperor Goose nests using hierarchical models and 24 years of nest monitoring data (1994–2017) from the Yukon–Kuskokwim Delta (Y–K Delta) in western Alaska. Our results indicate that overall nest survival was generally high (µ = 0.766, 95% CRI: 0.655–0.849) and ranged from 0.327 (95% CRI: 0.176–0.482) in 2013 to 0.905 (95% CRI: 0.839–0.953) in 1995. We found that DSPs of nests were influenced by nest traits, negatively influenced by major tidal flooding events and by researcher disturbance, but were not influenced by regional indices of spring timing, temperature and precipitation during nesting, or fox and vole abundance on the Y–K Delta. However, the number of nests found each year was negatively related to our index of fox abundance, suggesting nests that failed as a result of fox predation may have never been discovered due to our limited nest-searching efforts during egg laying. Our results suggest that regional environmental variation had minimal influence on the nest survival of Emperor Geese, although major flooding events were important. Nevertheless, we suspect that within-year variation in local weather conditions and local abundance of predators and alternative prey may be important and should be considered in future studies.
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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) | 0.001 | 0.001 |
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".