Not all seabird species can overcome marine threats when predator removal at their colonies is prioritised
Bibliographic record
Abstract
Abstract Seabirds are globally threatened. In the face of multiple threats, it is critical to understand how conservation strategies that mitigate one threat intersect with others to impact population viability. Marine threats, including pollution, climate change, and fisheries could derail gains to seabird populations resulting from arduous predator eradication efforts. However, this potentially negative effect is yet to be evaluated. We test whether mortality from marine threats can subvert the on-going recovery of 17 seabird species from 37 colonies on islands worldwide where predators were removed. We use demographic modelling to estimate potential adult mortality from fisheries, plastic ingestion, and climate change. For 82% of the species we examine, marine threats do not impede recovery following predator eradication. However, for six colonies of three species, Calonectris diomedea, C. borealis , and Ardenna carneipes , mortality from multiple marine threats may interrupt their recovery. Combining our demographic approach with comparative phylogenetic methods, we explore whether foraging niche, range, and morphometric traits inform the vulnerability to marine threats using an expanded dataset of 81 seabird species. Our analyses reveal surface filtering and pursuit diving species, and species with smaller at-sea distributions to be most vulnerable to declines due to multiple threats. However, these traits do not necessarily predict species’ vulnerability to marine threats in the absence of predators at nesting colonies, suggesting that shared traits may not be useful to infer vulnerability to multiple marine threats. Post-eradication monitoring to determine whether species require additional conservation management following predator eradication are essential in the face of intensifying pressures in the marine environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".