Simulated N and S deposition affected soil chemistry and understory plant communities in a boreal forest in western Canada
Bibliographic record
Abstract
We conducted a simulated nitrogen (N) and sulfur (S) deposition experiment from 2006 to 2012 to answer the following questions: (i) does chronic N and S deposition decrease cation concentrations in the soil and foliage of understory plant species, and (ii) does chronic N and S deposition decrease plant diversity and alter species composition of the understory plant community in a boreal forest in western Canada where intensifying industrial activities are increasing N and S deposition? Our field site was a mixedwood boreal forest stand located ~100 km southeast of Fort McMurray, Alberta, Canada. The experiment involved a 2 × 2 factorial design, with two levels each of N (0 and 30 kg N ha−1 yr−1; applied as NH4NO3) and S addition (0 and 30 kg S ha−1 yr−1; applied as Na2SO4). Four blocks were established in July 2006, each with four plots of 20 × 20 m randomly assigned to the treatments. Soil and understory vegetation were sampled and cover (%) of individual species of herb (height ≤ 0.5 m) and shrub (height 0.5–1 m) layers was determined in August 2012. Seven years after the treatments began, N addition increased dissolved organic carbon and N in the mineral soil (P < 0.05), whereas S addition decreased exchangeable cations (P < 0.05) in the forest floor. In the shrub layer, species evenness, and overall diversity were decreased by N addition (P < 0.05) due to increases in abundance of nitrophilous species and S addition (P < 0.01) due to decreased cation concentrations in soils. Total shrub cover decreased with S addition (P < 0.10). Nitrogen and S addition affected neither species richness nor evenness in the herb layer. However, permutational multivariate analysis of variance and non-metric multidimensional scaling analyses (based on plant cover) indicated that the effect of N and S addition on understory plant species composition in the both shrub and herb layers was species-specific. Addition of N decreased foliar phosphorus and potassium concentrations in some species, suggesting potential risk of N-meditated nutrient imbalance in those species. Our results indicate that long-term elevated levels of N and S deposition can negatively impact plant nutrition and decrease the diversity of the understory plant community in boreal forests in northern Alberta, Canada. However, considering that the current N and S deposition rates in northern Alberta are much lower than the rates used in this study, N and S deposition should not negatively affect plant diversity in the near future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".