A deadly cocktail: How a drought around 4200 cal. yr BP caused mass mortality events at the infamous ‘dodo swamp’ in Mauritius
Bibliographic record
Abstract
Analyses of pollen, diatoms, XRF geochemistry, and pigments provide a unique window into how an insular ecosystem in Mauritius responded to an extreme drought event 4200 years ago. We provide a reconstruction of regional vegetation change and local wetland development under influence of sea level rise and inferred climate change between 4400 and 4100 cal. yr BP. Our multi-proxy data evidence a severe drought between 4190 and 4130 cal. yr BP, which ultimately led to mass mortality of larger vertebrates, including two species of giant tortoises and dodos in a <2-ha region. This prolonged drought around the Indian Ocean is recorded in many regions dependent on monsoon precipitation and is suggested to cause the collapse of human societies in East Africa and India. We demonstrate a direct relation between the mass mortality events in the Mare aux Songes (MAS) rock valley and the 4200 cal. yr BP drought. MAS represents a fresh water source that attracted and concentrated vertebrates. Abrupt increased aridity induced regional fires on Mauritius and caused decreased water levels, and a shrinking water surface resulting in further concentration of the animals in this coastal site. Upconing of the saline wedge underlying the fresh water source induced progressive salinization. The excrements of the animals produced hypertrophic conditions that, combined with salinization and high temperatures, created a suitable environment for potentially toxic cyanobacteria. These factors led to a deadly cocktail, resulting in the death of 100,000s of vertebrates by intoxication, dehydration, trampling, and miring, and promoted a unique conservation of fossils. The ‘4.2 ka megadrought’ likely induced similar bottlenecks elsewhere in the SW Indian Ocean region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".