Algal reorganization in post-crisis Early Triassic oceans revealed by biomarker evidence
Bibliographic record
Abstract
The end-Permian mass extinction (EPME) fundamentally reshaped marine ecosystems. However, the long-term response of eukaryotic algae, a key foundation for marine primary production, is poorly understood. To address this limited knowledge, we determine the long-term change in algal communities using molecular fossil steranes. We use samples that span the uppermost Permian to the Lower Triassic from sections that were located in Boreal Sea (Sverdrup Basin, Arctic Canada) as well as the tropical Tethys (Xiakou, South China), and complement these new data with published datasets. Sterane to hopane ratios, reflecting the relative contribution of eukaryotic algal to bacterial sources, vary in absolute values between sites but show no significant decrease in the earliest Griesbachian compared to the pre-crisis Permian. However, Early Triassic ratios changed dramatically. In the Sverdrup Basin, they were stable during the Griesbachian and, following an interval where both hopane and sterane concentrations diminished, became much higher in the late Spathian. This confirms suggestions that there was a major decline in algal productivity after the EPME that may have delayed recovery. Sterane C 28 /C 29 ratios, which monitor algal composition, increase at the EPME level in Meishan and are generally higher in the rest of the Early Triassic in the Sverdrup Basin and Chaohu. The increase shows that algae that preferentially produce C 28 over C 29 sterols were thriving, possibly including those predominant in modern oceans. It further implies a reorganized marine algal community–apparently in the tropics and in the post-crisis interval in the Boreal realm. Our findings suggest that instead of a simple collapse and recovery, the Early Triassic saw a complicated reorganisation for algae.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".