Assessing the effects of environmentally-mediated phenological matching of an Arctic-breeding songbird to its arthropod prey
Bibliographic record
Abstract
Snow Buntings (Plectrophenax nivalis) are an Arctic-breeding cold-adapted songbird currently suffering from large population declines within the context of rapid environmental change. The goal of my thesis was to examine whether Snow Buntings can keep pace with the degree of climate change currently facing the Arctic. Specifically, we first examined whether Snow Buntings can phenologically adjust laying decisions to keep pace with climate change. We then studied the fitness consequences for phenological mismatches between Snow Buntings and arthropod prey by comparing how indices of temporal and biomass mismatch predicted offspring number and quality. To examine these questions, we used a long-term dataset (2007-2019) supplemented by two years of my own research (2022-23) from Qikiqtakuluk (East Bay Island), Nunavut to relate variation in environmental conditions and arthropod abundance to bunting lay date, clutch size, and success outcomes (e.g., fledging success and quality). In Chapter 2, we found that: i) Snow Buntings are extremely responsive to changes in temperature, ii) they rely on cues just days before investment in reproduction to time laying, and that iii) certain individuals can initiate laying at lower temperatures and therefore lay earlier than expected. In Chapter 3, we found that while a widely-used temporal mismatch index did not predict variation in breeding success, females timing breeding so that the peak of nestling demand matched higher arthropod biomass levels fledged the greatest number of offspring. However, neither the temporal nor biomass mismatch indices predicted fledgling quality. Together, these results are the first evidence of an Arctic breeding bird showing an adaptive response to climate change. For the first time, this thesis provides unique insight into the adaptive capacity of this Arctic passerine birds and suggests this species currently has the plasticity necessary to adapt to a rapidly changing Arctic.
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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 | 0.002 |
| 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 | 0.000 |
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".