Vegetation and fire history of the Lake Baikal Region since 32 ka BP reconstructed through microcharcoal and pollen analysis of lake sediment from Cis- and Trans-Baikal
Bibliographic record
Abstract
With the increase in global wildfire activity in response to global climate warming, the reconstruction of long-term fire histories and their links to environmental and anthropogenic factors has recently become an important focus of palaeoenvironmental research. Here we compare the precisely radiocarbon ( 14 C) dated long-term histories of vegetation change and fire activity from lakes Ochaul (Cis-Baikal) and Kotokel (Trans-Baikal) in the Lake Baikal Region (LBR) of Siberia, a known source region of wildfires whose past and future relationships with climate, vegetation structure and human economy are still poorly understood. Our results show that under cold and dry glacial climate conditions (32–18.2 ka BP) fire frequencies in both study regions were low. Deglaciation, which was characterised by the spread of woody plants, began around 18.2 ka BP, accompanied by a slight increase in fire activity. Differences in the fire records from both subregions are observed from the end of the Lateglacial (LG), with peak fire activity in Cis-Baikal during the Early Holocene (EH) and in Trans-Baikal during the Middle Holocene (MH). During the Late Holocene (LH) both regions are marked by generally low fire activity. We propose that the long-term spatiotemporal differences in fire activity during the EH–MH interval are primarily driven by vegetation composition and landscape openness and the resulting changes in fire regime. Interestingly, both peaks are also observed in a global-scale fire record, which suggests spatiotemporal complexity of the Holocene fire history. Low charcoal accumulation rates in both records during the Middle Neolithic (ca. 6660–6050 a BP) “cultural hiatus” archaeologically documented for Cis-Baikal suggest an LBR-wide population decline. On the other hand, the spread of Late Bronze and Iron Age cultures into the LBR from 3.5 ka BP may have at least partly driven the increase in fire frequency around Lake Kotokel. • Spatiotemporal complexity in the AMS-dated vegetation and fire records from Siberia. • Increase in fire activity coincides with the onset of deglaciation ca. 18.2 ka BP. • Peak fire activities occur in the Early (Cis-Baikal) and Middle Holocene (Trans-Baikal). • Climate change and vegetation composition controlled glacial-interglacial fire activity. • Holocene fire trends partially correlate with human activities in the Lake Baikal 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 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 | 0.001 | 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 | 0.002 |
| 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".