Sierra Nevada mountain lake microbial communities are structured by temperature, resources and geographic location
Bibliographic record
Abstract
Warming, eutrophication (nutrient fertilization) and brownification (increased loading of allochthonous organic matter) are three global trends impacting lake ecosystems. However, the independent and synergistic effects of resource addition and warming on autotrophic and heterotrophic microorganisms are largely unknown. In this study, we investigate the independent and interactive effects of temperature, dissolved organic carbon (DOC, both allochthonous and autochthonous) and nitrogen (N) supply, in addition to the effect of spatial variables, on the composition, richness, and evenness of prokaryotic and eukaryotic microbial communities in lakes across elevation and N deposition gradients in the Sierra Nevada mountains of California, USA. We found that both prokaryotic and eukaryotic communities are structured by temperature, terrestrial (allochthonous) DOC and latitude. Prokaryotic communities are also influenced by total and aquatic (autochthonous) DOC, while eukaryotic communities are also structured by nitrate. Additionally, increasing N availability was associated with reduced richness of prokaryotic communities, and both lower richness and evenness of eukaryotes. We did not detect any synergistic or antagonistic effects as there were no interactions among temperature and resource variables. Together, our results suggest that (a) organic and inorganic resources, temperature, and geographic location (based on latitude and longitude) independently influence lake microbial communities; and (b) increasing N supply due to atmospheric N deposition may reduce richness of both prokaryotic and eukaryotic microbes, probably by reducing niche dimensionality. Our study provides insight into abiotic processes structuring microbial communities across environmental gradients and their potential roles in material and energy fluxes within and between ecosystems.
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 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 | 0.000 | 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) | 0.000 | 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".