Development of anchialine cave habitats and karst subterranean estuaries since the last ice age
Bibliographic record
Abstract
Abstract Extinction models generally predict that coastal and neritic fauna benefit during sea-level rise (transgression), whereas sea-level retreat (regression) diminishes their suitable habitat area and promotes evolutionary bottlenecks. Sea-level change also impacts terrestrial island biogeography, but it remains a challenge to evidence how sea-level rise impacts aquatic island biogeography, especially in the subterranean realm. Karst subterranean estuaries (KSEs) occur globally on carbonate islands and platforms, and they are populated by globally-dispersed, ancient ecosystems (termed anchialine). Anchialine fauna currently exhibit a disjunct biogeography that cannot be completely explained by plate tectonic-imposed vicariance. Here we provide evidence that anchialine ecosystems can experience evolutionary bottlenecks caused by habitat reduction during transgression events. Marine-adapted anchialine fauna benefit from habitat expansion during transgressions, but fresh- and brackish-adapted fauna must emigrate, evolve to accommodate local habitat changes, or are regionally eliminated. Phanerozoic transgressions relative to long-term changes in subsidence and relief of regional lithology must be considered for explaining biogeography, evolution, local extirpation or complete extinction of anchialine fauna. Despite the omission of this entire category of environments and animals in climate change risk assessments, the results indicate that anchialine fauna on low-lying islands and platforms that depend upon meteoric groundwater are vulnerable to habitat changes caused by 21st century sea-level rise.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".