Foraging tactics in dynamic sea‐ice habitats affect individual state in a long‐ranging seabird
Bibliographic record
Abstract
Abstract Individual heterogeneity in diet and foraging behaviour is common in wild animal populations, and can be a strong determinant of how populations respond to environmental changes. Within populations, variation in foraging behaviour and the occurrence of individual tactics in relation to resources distribution can help explain differences in individual fitness, and ultimately identify important factors affecting population dynamics. We examined how foraging behaviour and habitat during the breeding period related to the physiological state of a long‐ranging seabird adapted to sea ice, the Antarctic petrel Thalassoica antarctica . Firstly, using GPS tracking and state‐switching movement modelling (hidden Markov models) on 124 individual birds, we tested for the occurrence of distinct foraging tactics within our study population. Our results highlight a large variation in the movement and foraging behaviour of a very mobile seabird, and delineate distinct foraging tactics along a gradient from foraging in dense pack ice to foraging in open water. Secondly, we investigated the effects of these foraging tactics on individual state at return from a foraging trip. We combined movement data with morphometric and physiological measurements of a suite of plasma metabolites that provided a general picture of a bird's individual state. Foraging in denser sea ice was associated with lower gain in body mass during brooding, as well as lower level of energy acquisition (plasma triacylglycerol) during both brooding and incubation. We found no clear relationship between the foraging tactic in relation to sea ice and the energetic stress (changes in plasma corticosterone), energetic balance (β‐hydroxybutyrate) or trophic level (δ 15 N). However, a shorter foraging range was related to both the energetic balance (positively) and the trophic level (negatively). Our results highlight a diverse range of foraging tactics in relation to sea ice in Antarctic petrels. While the various foraging tactics do not seem to strongly alter energetic balance, they may affect other aspects of Antarctic petrels' physiology. Future changes in sea‐ice habitats can thus be expected to have an impact on the individual state of seabirds such as Antarctic petrels, which could ultimately affect their population dynamics. Nonetheless, strong individual heterogeneity in the use of sea‐ice habitats by a typical pagophilic species might strengthen its resilience to environmental changes and in particular to forecasted sea‐ice loss. A free Plain Language Summary can be found within the Supporting Information of this article.
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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) | 0.003 | 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; both teacher heads agree on what is shown here.
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".