Spatial and temporal variation in soil biogenic silicon, total nitrogen and total carbon over a twenty-one-year period in a hardwood forest of southwestern Quebec
Bibliographic record
Abstract
The biological cycling of silicon (Si) in terrestrial ecosystems is a key driver of long-term carbon sequestration, and terrestrial and marine productivity. A better knowledge of the effects of tree species composition on the spatial and temporal variation of biogenic silicon (BSi) in forest soils is essential to fully understand forests' effects on Si and carbon (C) cycling. In this study, I measured soil BSi concentration in the hardwood forest of the Morgan Arboretum (Montreal, QC, Canada) to understand its relationship with soil fertility parameters (pH, available P, K, Ca, Mg, Al, total C, total N, and water content), soil texture and tree species composition. I hypothesized that BSi would be higher under active Si accumulating species (maples and beech) and in clay textured soils regardless of other soil properties. Additionally, I explored the relationships between the changes in summer rainfall, temperature, and species composition with changes in soil BSi, total N, and total Cover the 1998-2019 period. Soil pH and Al, Fagus grandifolia's mean weighted DBH, and Acer rubrum's mean weighted DBH were the best predictors of soil BSi levels. Although clay content was also a significant predictor of soil BSi, it was more closely associated with soil fertility parameters. Overall, soil BSi tended to decrease with increases in Acer saccharum weighted DBH, a species commonly associated with active Si accumulation. I concluded that soil BSi is not necessarily high under active Si accumulating species and that predicting high soil BSi levels requires considering various ecosystem parameters and their interactions.Total N decreased in the forest from 1998 to 2019, while significant changes in soil BSi and total C were only detected in plots on the glacial till deposit. Extensive Fraxinus americana mortality and its replacement by A. saccharum drove the decrease in soil BSi. In contrast, soil N decrease was attributed to the significant increase of sugar maple in the forest, which has been associated to increases in nitrate, the form of nitrogen most susceptible to leaching. Finally, soil C decreases in the till deposit was attributed to soil and canopy changes driven by ash mortality and increases in soil water content and air temperature. Together, our results suggest that changes in the proportion of two species with different BSi uptake mechanisms, increases in water content and in air temperature in the last 21 years (1998-2019) have affected soil BSi, total C, and total N
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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) | 0.001 | 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 | 0.001 | 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".