Nutrient Enrichment and Connectivity Jointly Shape Bacterioplankton Diversity
Bibliographic record
Abstract
Abstract It is increasingly important to understand the response of freshwater communities and ecosystems to fertilizers given their widespread usage and the propensity for these fertilizers to runoff into rivers and lakes. Dispersal, an important ecological factor mediated by landscape connectivity, could potentially counteract the impacts of anthropogenic stressors through the reintroduction of communities unperturbed by local stressors. However, this potential has not yet been studied in the context of nutrient stressed natural communities. Here, we investigate the impacts of nutrient enrichment and connectivity on freshwater bacterioplankton communities. We subjected mesocosms stocked with native bacterioplankton communities to different combinations of nutrient enrichment and connectivity (volumes of water transferred between mesocosms). We show that nutrient enrichment strongly structures the bacterioplankton community, favoring nutrient tolerant taxa and depressing taxonomic diversity. Connectivity, however, interacts with nutrient enrichment to restore functional diversity in communities subjected to the highest levels of nutrient stress. Despite the ameliorating effects of dispersal, nutrient enrichment leaves a consistent signature in communities, driving a shift from more heterotrophic to more phototrophic communities. Taken together, our results demonstrate that while nutrient enrichment significantly impacts freshwater bacterioplankton communities, connectivity can help restore functional diversity to a certain extent.
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) | 0.001 | 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".