Temporal succession of bacterial and archaeal communities in a Mediterranean high-mountain lake over the last 430 years using sedimentary DNA
Bibliographic record
Abstract
Despite the known influence of climate change on high-altitude ecosystems, the long-term response of prokaryotic communities in Mediterranean high-mountain lakes remains poorly understood. Here, we investigate the temporal dynamics of prokaryotic communities over the past ~ 430 years in a Mediterranean high-mountain lake, utilizing sedimentary ancient DNA (sedDNA). By examining a sediment core from Borreguil Lake in the Sierra Nevada (Spain), we evaluated bacterial and archaeal abundance, diversity, and community composition (β-diversity) in relation to paleoenvironmental and climate data. Our findings revealed a significant restructuring of prokaryotic communities, particularly since ca. 1960. A Random Forest model identified dissolved organic carbon, organic nitrogen, Saharan atmospheric dust inputs, and temperature as key drivers of the abundance, diversity, and composition of prokaryotic communities, particularly in the modern era. Notably, the abundance and diversity of bacterial communities increased in response to increased dissolved organic carbon, elevated temperatures, and dust deposition, while archaea demonstrated a more nuanced response linked to organic nitrogen availability and dust inputs. The temporal shifts in microbial community composition point to broader ecological changes within the lake, shaped by climate-driven environmental variations. For example, the increased relative abundance of Cyanobacteria and other taxa linked to higher nutrient availability indicates ongoing eutrophication processes, likely intensified by climate warming. This study highlights the importance of high-mountain lakes as indicators of climate change, contributing valuable insights into microbial ecology's response to long-term environmental change. Our findings offer a foundational understanding for predicting microbial responses in sensitive ecosystems under future climate scenarios.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".