Local eukaryotic and bacterial stream community assembly is shaped by regional land use effects
Bibliographic record
Abstract
With anticipated expansion of agricultural areas for food production and increasing intensity of pressures stemming from land-use, it is critical to better understand how species respond to land-use change. This is particularly true for microbial communities which provide key ecosystem functions and display fastest responses to environmental change. However, regional land-use effects on local environmental conditions are often neglected, and, hence, underestimated when investigating community responses. Here we show that the effects stemming from agricultural and forested land use are strongest reflected in water conductivity, pH and phosphorus concentration, shaping microbial communities and their assembly processes. Using a joint species distribution modelling framework with community data based on metabarcoding, we quantify the contribution of land-use types in determining local environmental variables and uncover the impact of both, land-use, and local environment, on microbial stream communities. We found that community assembly is closely linked to land-use type but that the local environment strongly mediates the effects of land-use, resulting in systematic variation of taxon responses to environmental conditions, depending on their domain (bacteria vs. eukaryote) and trophic mode (autotrophy vs. heterotrophy). Given that regional land-use type strongly shapes local environments, it is paramount to consider its key role in shaping local stream communities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".