Dynamics of pre-breeding nutrient reserves in subarctic staging Lesser Snow Geese (Anser caerulescens caerulescens) and Ross’s Geese (Anser rossii): implications for reproduction
Bibliographic record
Abstract
Phenological mismatch occurs when the timing of reproductive events and seasonal availability of resources become decoupled, and is increasing in migratory birds in response to climate change. Arctic-nesting geese acquire important nutrient reserves at spring-staging areas, but advancing springs, combined with large population increases and long-term goose-mediated habitat alteration, could induce a mismatch between optimal timing of nesting and accumulation of required reserves. From 2012 to 2019, we randomly sampled Lesser Snow (<em>Anser caerulescens caerulescens</em>) and Ross’s Geese (<em>Anser rossii</em>) throughout their spring migration near Churchill, Manitoba, Canada. Our objectives were to evaluate patterns of protein and lipid accumulation, and draw comparisons to historical estimates obtained before widespread habitat changes. We found significantly reduced pre-breeding protein reserves in Lesser Snow Geese relative to their historical values, but average protein reserve size increased somewhat with declining population size during the years of our study. Lesser Snow Geese catabolized lipid reserves, and no longer increased protein reserves during spring staging. Protein reserves were on average 17–23% smaller in 2012–2019 than in the 1970s, the deficit being equivalent to the amount of protein found in two to three eggs. In contrast, Ross’s Geese maintained lipid reserves, and accumulated protein during the staging period, irrespective of annual Lesser Snow Geese abundance. Based on patterns in gizzard hypertrophy, they appeared to be using a more digestible food source than were Lesser Snow Geese. Declines in protein reserves may hinder the ability for Lesser Snow Geese to keep nutritional pace with increasingly early springs, and could be an important underlying mechanism driving mismatches between vegetation phenology and emergence of goslings. Differences in recruitment between Lesser Snow Geese and Ross’s Geese could be related to differences in nutrient-reserve dynamics during the final stages of spring migration, which can carry over to influence annual breeding probability, clutch size, nest success, and likelihood of mismatch at a population level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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) | 0.000 | 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".