High speciation rate of niche specialists in hot springs
Bibliographic record
Abstract
Ecological and evolutionary processes simultaneously regulate microbial diversity, but the evolutionary processes and their driving forces remain largely unexplored. Here we investigated the ecological and evolutionary characteristics of microbiota in hot springs spanning a broad temperature range (54.8-80 °C) by sequencing the 16S rRNA genes. Our results demonstrated that niche specialists and niche generalists are embedded in a complex interaction of ecological and evolutionary dynamics. On the thermal tolerance niche axis, thermal (T) sensitive (at a specific temperature) versus T-resistant (at least in five temperatures) species were characterized by different niche breadth, community abundance and dispersal potential, consequently differing in potential evolutionary trajectory. The niche-specialized T-sensitive species experienced strong temperature barriers, leading to completely species shift and high fitness but low abundant communities at each temperature ("home niche"), and such trade-offs thus reinforced peak performance, as evidenced by high speciation across temperatures and increasing diversification potential with temperature. In contrast, T-resistant species are advantageous of niche expansion but with poor local performance, as shown by wide niche breadth with high extinction, indicating these niche generalists are "jack-of-all-trades, master-of-none". Despite of such differences, the T-sensitive and T-resistant species are evolutionarily interacted. Specifically, the continuous transition from T-sensitive to T-resistant species insured the exclusion probability of T-resistant species at a relatively constant level across temperatures. The co-evolution and co-adaptation of T-sensitive and T-resistant species were in line with the red queen theory. Collectively, our findings demonstrate that high speciation of niche specialists could alleviate the environmental-filtering-induced negative effect on diversity.
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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 | 0.000 |
| 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) | 0.005 | 0.001 |
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".