Ecosystem-Level Responses to Increased Atmospheric Nitrogen Deposition in an Ontario Hayfield of Varying Soil Texture
Bibliographic record
Abstract
Atmospheric deposition of nitrogen (N) derived from agricultural intensification (fertilizer addition) and fossil fuel burning is likely to significantly affect the long-term stability of many ecosystems because it impacts plant growth, species diversity, and overall carbon and nutrient cycling. The characteristically high biomass and substantial belowground inputs to the soil carbon pool of semi-natural grassland ecosystems suggest that they could be one of the most important terrestrial carbon sinks for future climate change mitigation. However, the long-term consequences of enhanced atmospheric N deposition on such grasslands are difficult to predict because their responses to increased low-level N availability may be strongly influenced by factors such as soil texture that have not been investigated. The objective of this research was to determine the impacts of 16 years of low-level experimental N addition (simulating 2050 atmospheric N input rates) to a hayfield of varying soil texture (clay loam – sandy loam) on species diversity, above- and belowground biomass and ecosystem carbon dioxide fluxes. The chronic low-level N additions had no significant impacts on growth of any plant species or on carbon dioxide fluxes. Instead, ecosystem carbon dioxide fluxes over the growing season were primarily influenced by soil texture, soil temperature, and sampling date. Aboveground biomass and species diversity were best explained by variation in mean growing season soil moisture, while belowground biomass did not vary in response to any measured environmental parameter (including soil texture). The consistent lack of responses to the N addition treatment indicates that future increases in atmospheric N deposition are unlikely to have major impacts on Ontario’s hayfields. By contrast, the strong interconnected influences of soil texture and soil moisture on multiple community and ecosystem-level properties suggest that the consequences of anticipated future declines in summer precipitation on hayfield vegetation will be strongly influenced by soil texture. Therefore, since the soils in this region are heterogeneous in texture at multiple scales, future climate changes are likely to have substantial, but spatially variable, impacts on plant community structure and carbon balance in its hayfield ecosystems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".