Predicting the Foraging Habitats of Sympatrically Breeding Gadfly Petrels in the South Pacific Ocean
Bibliographic record
Abstract
Gadfly petrels (genus Pterodroma ) are one of the most threatened groups of birds. They are exceptionally well adapted to forage over enormous areas to maximize chances of encountering prey. Their wide-ranging travel, extensive use of oceanic habitats beyond national jurisdictions (the high seas), and limited information on their at-sea distributions and foraging ecology pose several management challenges. Here, we examined the foraging distributions and habitat preferences of three gadfly petrels that breed on Phillip Island (Norfolk Island Group), in the southwest Pacific Ocean, and tested the ability of species distribution models (SDMs) to predict important marine habitats. GPS loggers were deployed in 2018 and 2019 on chick-provisioning black-winged petrels ( P. nigripennis ) and white-necked petrels ( P. cervicalis ) and in 2020 on Kermadec petrels ( P. neglecta ), and hidden Markov models (HMMs) were used to estimate behavioral states across 387 foraging trips. SDMs were built using six algorithms and the predictive performance of models constructed using conventional random cross-validation (CV) was compared to those constructed with spatially independent CV. All three species demonstrated dual-foraging strategies with short trips closer to the colony and longer, presumably self-provisioning, trips with maximum distances from the colony of several thousand kilometers for black-winged and white-necked petrels. Foraging areas of each species were distinctly partitioned across the Tasman Sea during long trips, but there was high overlap during short trips. Black-winged and white-necked petrels exhibited area-restricted search foraging behavior throughout their foraging ranges which spanned almost the entire Tasman Sea and into the western Pacific, whereas the foraging range of Kermadec petrels was restricted closer to the colony. Approximately half of each species’ foraging range extended into the high seas. Response curves and variable importance between the two SDM CV approaches were similar, suggesting that model fitting was robust to the CV approach. However, evaluation using spatially independent CV indicated that generalizability of ensemble SDMs to new data ranged from poor to fair for all three species. This suggests that the maximal-area foraging strategy of gadfly petrels (whereby they search opportunistically for resources across expansive oceanic habitats) results in weak or wide associations with environmental features making predicting important habitats extremely challenging.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".