Point Counts Underestimate the Importance of Arctic Foxes as Avian Nest Predators: Evidence from Remote Video Cameras in Arctic Alaskan Oil Fields
Bibliographic record
Abstract
We used video cameras to identify nest predators at active shorebird and passerine nests and conducted point count surveys separately to determine species richness and detection frequency of potential nest predators in the Prudhoe Bay region of Alaska. From the surveys, we identified 16 potential nest predators, with glaucous gulls (Larus hyperboreus) and parasitic jaegers (Stercorarius parasiticus) making up more than 80% of the observations. From the video evidence, however, we identified arctic foxes (Alopex lagopus) as the predators in five of six predation events recorded with the cameras. These results indicate that estimated abundances of predators alone may not accurately reflect their true or proportional importance as nest predators. We also found that the identified predators removed all eggs and left the nests intact. Thus, attempts to identify predators solely on the basis of nest remains are not reliable for smaller bird species in this region. We found no evidence that camera-monitored nests were at greater risk of predation or desertion than camera-free nests. Overall, our ability to film predation events was hampered by the brief, highly synchronized breeding season, the harsh climate, and the higher nest survivorship for shorebirds in this region relative to temperate-breeding passerines, which have been the focus of most studies that use camera systems in attempts to identify nest predators at active nests.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".