Marine extinction risk shaped by trait–environment interactions over 500 million years
Bibliographic record
Abstract
Perhaps the most pressing issue in predicting biotic responses to present and future global change is understanding how environmental factors shape the relationship between ecological traits and extinction risk. The fossil record provides millions of years of insight into how extinction selectivity (i.e., differential extinction risk) is shaped by interactions between ecological traits and environmental conditions. Numerous paleontological studies have examined trait-based extinction selectivity; however, the extent to which these patterns are shaped by environmental conditions is poorly understood due to a lack of quantitative synthesis across studies. We conducted a meta-analysis of published studies on fossil marine bivalves and gastropods that span 458 million years to uncover how global environmental and geochemical changes covary with trait-based extinction selectivity. We focused on geographic range size and life habit (i.e., infaunal vs. epifaunal), two of the most important and commonly examined predictors of extinction selectivity. We used geochemical proxies related to global climate, as well as indicators of ocean acidification, to infer average global environmental conditions. Life-habit selectivity is weakly dependent on environmental conditions, with infaunal species relatively buffered from extinction during warmer climate states. In contrast, the odds of taxa with broad geographic ranges surviving an extinction (>2500 km for genera, >500 km for species) are on average three times greater than narrow-ranging taxa (estimate of odds ratio: 2.8, 95% confidence interval = 2.3-3.5), regardless of the prevailing global environmental conditions. The environmental independence of geographic range size extinction selectivity emphasizes the critical role of geographic range size in setting conservation priorities.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".