Atmospheric nitrogen deposition is related to plant biodiversity loss at multiple spatial scales
Bibliographic record
Abstract
Due to various human activities, including intensive agriculture, traffic, and the burning of fossil fuels, in many parts of the world, current levels of reactive nitrogen emissions strongly exceed pre-industrial levels. Previous studies have shown that the atmospheric deposition of these excess nitrogen compounds onto semi-natural terrestrial environments has negative consequences for plant diversity. However, these previous studies mostly investigated biodiversity loss at local spatial scales, that is, at the scales of plots of typically a few square meters. Whether increased atmospheric nitrogen deposition also affects plant diversity at larger spatial scales remains unknown. Here, using grassland plant community data collected in 765 plots, across 153 different sites and 9 countries in northwestern Europe, we investigate whether relationships between atmospheric nitrogen deposition and plant biodiversity are scale-dependent. We found that high levels of atmospheric nitrogen deposition were associated with low levels of plant species richness at the plot scale but also at the scale of sites and regions. The presence of 39% of plant species was negatively associated with increasing levels of nitrogen deposition at large (site) scales, while only 1.5% of the species became more common with increasing nitrogen deposition, indicating that large-scale biodiversity changes were mostly driven by "loser" species, while "winner" species profiting from high N deposition were rare. Some of the "loser" species whose site presence was negatively associated with atmospheric nitrogen deposition are listed as "threatened" in at least some EU member states, suggesting that nitrogen deposition may be a key contributor to their threat status. Hence, reductions in reactive nitrogen emissions will likely benefit plant diversity not only at local but also at larger spatial scales.
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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 | 0.001 |
| 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 | 0.000 | 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".