Carry-over Effects in Arctic-breeding Shorebirds: A Cross-species Perspective
Bibliographic record
Abstract
Avian studies have long been limited to single populations at a single time and place.However, such studies overlook carry-over effects, where conditions in one season cause fitness consequences in subsequent seasons.As technological advances make it possible to follow individual birds over a full year, it has become clear that carry-over effects can have fitness implications, and are therefore important to consider.In this thesis, I use tracking and physiological data from 14 species of Arctic-breeding shorebirds to link reproductive and timing variation to a bird's earlier experiences.In general, carry-over effects appear to influence important metrics of breeding and timing in Arctic-breeding shorebirds.The timing of nest initiation was influenced by both prior conditions, inferred through migration timing, and local weather conditions.While patterns were generally consistent across species, variation in the influence of carry-over effects among species merits further research.Tracking data also showed that delays in one season continue into the next, although seasonally variable mitigation means that birds generally reduced the extent of delays, potentially at a physiological cost.Winter is the exception, as birds appear able to fully "reset the clock" during this period, preventing delays from accumulating across years.Winter levels of the stress hormone corticosterone (CORT) in feathers showed a positive relationship with nest success in the subsequent summer, supporting the idea that high levels of CORT may not always imply that an individual is struggling, or at least that the relationship between CORT and fitness may be complex.This thesis is one of the first multi-species studies of carry-over effects, and is unparalleled in the number of species and sample size within the carry-over effect iii literature.It is additionally novel for the multiple methods used to assess carry-over effects across a similar group of species.The importance of carry-over effects demonstrated within this thesis highlights the need for using a whole year approach to assess what influences variation in fitness, especially in migratory species.Doing so will improve our ability to identify and understand the causes of factors affecting demographic rates and driving declines across taxa.Many people have contributed to this thesis, but before I list them, I wish to acknowledge the birds who have inspired me through their epic migrations and toughness in the face of so many challenges.Through my research and other work, I hope to help alleviate some of those challenges in at least some small way.Many thanks to my supervisors, Dr. Paul Smith and Dr. Joe Bennett
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".