Photogrammetry and multievent modeling to determine effects of disturbance on seabird colony nest success
Bibliographic record
Abstract
Seabirds are sentinel species, with shifts in their demographics reflecting the cumulative pressures facing global marine ecosystems. However, studying their reproductive success is often limited by the difficulty of continuous observation throughout the breeding season. To address this, we deployed automated cameras in remote field sites to capture comprehensive breeding season data and quantify the impact of predators on colonial nesting seabirds. We attached remote cameras, adjacent to 2 double-crested cormorant Nannopterum auritum cliff-nesting colonies, to monitor the northern range limit of the Pacific subspecies (N. auritum albociliatum), a species at-risk in British Columbia, Canada. We documented the effect of predator presence and flushing events (disturbances which cause birds to leave their nest) at the double-crested cormorant colonies, which facilitate further ancillary predation by opportunistic gulls and crows. We fitted multievent capture-recapture models to compare the effect of nest flushing between years on weekly nest survival and seasonal nest success. It was found that flushing events led to complete breeding failure at one site across all years, while the second site showed variable nest success related to predator presence. Frequent flushing events were associated with nest abandonment, lower egg survival, and delays in nest transitions between egg, chick, and fledgling life stages. Pairing nest photogrammetry with multievent models is applicable to other colonial species and predators and may help us to better understand local recruitment, mortality, and predation effects on the growth and recovery of a species across breeding seasons.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".