Think global, act local: The small‐scale environment mainly influences microbial community development and function in lake sediment
Bibliographic record
Abstract
Abstract The early stages of community development influence longer‐term establishment of species, traits, and ultimately ecosystem function. How this process varies with small‐ and large‐scale abiotic and biotic conditions is poorly studied in microbes. Here, we tested how different spatial scales influenced the rate at which taxonomic and functional composition of microbial communities changed over time in lake sediments, and whether these changes occurred synchronously across different environments given the same initial communities. We manipulated the small‐scale environment by creating sediments with different terrestrial organic matter (t‐OM) quantity and quality, and placing these in two lakes differing in trophic status to vary the large‐scale environment. We found that archaeal and bacterial communities, but not fungi, became taxonomically dissimilar over 2 months despite being derived from the same initial leaf material, primarily because of small‐scale environmental conditions. Sediment t‐OM quantity consistently explained changes in community composition both temporally within mesocosms and spatially between mesocosms in different lakes. Archaea, bacteria, and fungi also varied by up to 10 times in how quickly they changed, providing among the first evidence in the same study system that they respond differently over time to abiotic and biotic conditions. Finally, functional composition was influenced by both small‐ and large‐scale environmental conditions, with genes involved in t‐OM decomposition showing some of the largest changes in abundance after 1 yr. Our study highlights that future changes to both sediments and lake waters can modify how sediment microbial communities develop with consequences for important ecosystem functions like carbon cycling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".