High‐resolution tide projections reveal extinction threshold in response to sea‐level rise
Bibliographic record
Abstract
Sea-level rise will affect coastal species worldwide, but models that aim to predict these effects are typically based on simple measures of sea level that do not capture its inherent complexity, especially variation over timescales shorter than 1 year. Coastal species might be most affected, however, by floods that exceed a critical threshold. The frequency and duration of such floods may be more important to population dynamics than mean measures of sea level. In particular, the potential for changes in the frequency and duration of flooding events to result in nonlinear population responses or biological thresholds merits further research, but may require that models incorporate greater resolution in sea level than is typically used. We created population simulations for a threatened songbird, the saltmarsh sparrow (Ammodramus caudacutus), in a region where sea level is predictable with high accuracy and precision. We show that incorporating the timing of semidiurnal high tide events throughout the breeding season, including how this timing is affected by mean sea-level rise, predicts a reproductive threshold that is likely to cause a rapid demographic shift. This shift is likely to threaten the persistence of saltmarsh sparrows beyond 2060 and could cause extinction as soon as 2035. Neither extinction date nor the population trajectory was sensitive to the emissions scenarios underlying sea-level projections, as most of the population decline occurred before scenarios diverge. Our results suggest that the variation and complexity of climate-driven variables could be important for understanding the potential responses of coastal species to sea-level rise, especially for species that rely on coastal areas for reproduction.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".