Will the Effects of Sea-Level Rise Create Ecological Traps for Pacific Island Seabirds?
Bibliographic record
Abstract
More than 18 million seabirds nest on 58 Pacific islands protected within vast U.S. Marine National Monuments (1.9 million km2). However, most of these seabird colonies are on low-elevation islands and sea-level rise (SLR) and accompanying high-water perturbations are predicted to escalate with climate change. To understand how SLR may impact protected islands and insular biodiversity, we modeled inundation and wave-driven flooding of a globally important seabird rookery in the subtropical Pacific. We acquired new high-resolution Digital Elevation Models (DEMs) and used the Delft3D wave model and ArcGIS to model wave heights and inundation for a range of SLR scenarios (+0.5, +1.0, +1.5, and +2.0 m) at Midway Atoll. Next, we classified vegetation to delineate habitat exposure to inundation and identified how breeding phenology, colony synchrony, and life history traits affect species-specific sensitivity. We identified 3 of 13 species as highly vulnerable to SLR in the Hawaiian Islands and quantified their atoll-wide distribution (Laysan albatross, Phoebastria immutabilis; black-footed albatross, P. nigripes; and Bonin petrel, Pterodroma hypoleuca). Our models of wave-driven flooding forecast nest losses up to 10% greater than passive inundation models at +1.0 m SLR. At projections of + 2.0 m SLR, approximately 60% of albatross and 44% of Bonin petrel nests were overwashed displacing more than 616,400 breeding albatrosses and petrels. Habitat loss due to passive SLR may decrease the carrying capacity of some islands to support seabird colonies, while sudden high-water events directly reduce survival and reproduction. This is the first study to simulate wave-driven flooding and the combined impacts of SLR, groundwater rise, and storm waves on seabird colonies. Our results highlight the need for early climate change planning and restoration of higher elevation seabird refugia to prevent low-lying protected islands from becoming ecological traps in the face of rising sea levels.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".