Spatial heterogeneity in soil pyrogenic carbon mediates tree growth and physiology following wildfire
Bibliographic record
Abstract
Abstract Pyrogenic carbon (PyC) is a ubiquitous legacy of wildfire in terrestrial soils, yet how it affects the growth and function of regenerating plants has received little research attention. We examined responses to a natural gradient of PyC deposition 5 years following a severe fire in a northern boreal forest, based on measurements of growth (height, basal area increment and leader extension), physiological performance ( F v/ F m) and foliar nutrition (C, N, P, K, Mg) of Pinus banksiana Lamb. We determined the concentration of PyC, expressed as a dosage (t/ha, in mineral soils collected from the rootzones of each sapling and used it as an independent factor to model trait responses to increasing PyC levels, in conjunction with measurements of soil physio‐chemical properties (pH, EC, VOC, Ash, N, P, K, Ca and Mg). Quantification and spatial analysis of PyC reveals heterogeneous deposition across the landscape with fine‐grained patchiness at scales <0.5 m. In response to this heterogeneity, phenotypic and nutritional adjustments followed dose‐dependent response patterns. Beneficial effects of PyC on sapling growth occurred to an optimum point of ~30–60 t/ha, while declining patterns were found for trees in dosages exceeding 100 t/ha. Some traits were positively and negatively related to soil K and N, respectively, and shared strong negative associations with soil pH and volatile matter. Synthesis . This study supports the longstanding hypothesis that soil PyC enhances growth and physiological function of fire‐adapted plants, but indicates that responses are highly dosage‐dependent, with natural levels of PyC deposition commonly exceeding an optimum point. These results also suggest that the main mechanisms for observed responses to PyC include: (a) enhanced supply of base cations, (b) immobilization of N and (c) pronounced liming. Future changes in climate are expected to increase fire frequency, particularly in circumpolar boreal forests. We predict shifts in PyC to frequently exceed the threshold resulting in reduced plant growth and ultimately ecosystem productivity.
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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".