Regime shift of bacterial communities in lake ecosystems in the arid and semi-arid north-west of China: Evidence from the sedimentary archives
Bibliographic record
Abstract
• The concentrations of heavy metals significantly increased after 1950s. • Bacterial communities showed a regime shift and in Lake Bosten after 1960s. • Bacterial communities exhibited a stable-unstable-stable progress during the regime shift. • Stochastic processes mainly prevail in the transitional unstable phase. Since the middle of the last century, China has experienced social-economic progress and climate transition, which have been strong triggers for the regime shift of lake ecosystem, threatening species coexistence, biodiversity and community persistence. Given the high vulnerability of ecosystems in arid and semi-arid north-west of China, anticipating their regime shifts can contribute to the development of effective interventions to maintain lake health. However, it is not known how and to what extent lake ecosystems have changed in this region. To fill this gap, we investigated the imprints in paleolimnological sedimental cores over the last 150 ∼ 200 years in shallow Lake Bosten and deep Lake Sayram. Results showed that anthropogenic heavy metals showed a sudden increase along the sediment cores. In Lake Bosten, bacterial diversity, niche differentiation, and species interactions exhibited a stepwise shift from an alternative state to anther in a nonlinear manner, highlighting the existence of regime shift. In contrast, in Lake Sayram, the change in bacterial communities was more gradual. Compared with the two alternative states, network topology analysis revealed tighter bacterial interactions in the intermediate transitional phase, which implicates stable-unstable-stable progresses during the regime shift. Concurrently, the predominant deterministic processes in the two alternative states and stochastic processes in the transitional phase may reflect the important roles of stochasticity in triggering the regime shift. Overall, our study showed that anthropogenic activities lead to a regime shift in the shallow lake rather but not in the deep lake.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".