A large-scale assessment of lakes reveals a pervasive signal of land use on bacterial communities
Bibliographic record
Abstract
Abstract Lakes play a pivotal role in ecological and biogeochemical processes and have been described as “sentinels” of environmental change. Assessing “lake health” across large geographic scales is critical to predict the stability of their ecosystem services and their vulnerability to anthropogenic disturbances. The LakePulse research network is tasked with the assessment of lake health across gradients of land use on a continental scale. Bacterial communities are an integral and rapidly responding component of lake ecosystems, yet large-scale responses to anthropogenic activity remain elusive. Here, we assess the ecological impact of land use on bacterial communities from over 200 lakes covering more than 660,000 km2 across Eastern Canada. In addition to community variation between ecozones, land use across Eastern Canada also appeared to alter diversity, community composition, and network structure. Specifically, increasing anthropogenic impact within the watershed lowered diversity. Likewise, community composition was significantly correlated with agriculture and urban development within a watershed. Interaction networks showed decreasing complexity and fewer keystone taxa in impacted lakes. Moreover, we identified potential indicator taxa of high or low lake water quality. Together, these findings point to detectable bacterial community changes of largely unknown consequences induced by human activity within lake watersheds.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".