Recovery from fire affects spatial variability of nutrient availability in boreal aspen ecosystems
Bibliographic record
Abstract
Fire is a key driver of nutrient biogeochemistry in boreal ecosystems. Although a significant amount of research has been conducted to understand boreal fire ecology, it is still unclear how fire affects the spatial distribution of nutrients and what mechanisms are responsible for the post-fire recovery of spatial patterns. In this study, we examined spatial variability in soil nutrient bioavailability and related aboveground (AG) and belowground (BG) properties in three boreal aspen (Populus tremuloides Michx.) stands in northern Alberta at different stages of post-fire recovery. The studied sites include a 1-year old post fire stand (PF), a 9-year old stand at canopy closure (CC), and a 72-year old mature stand (MA). Ion exchange resin was used to measure nutrient bioavailability in-situ and was related to AG (vegetation and forest floor characteristics) and BG (soil microbial and chemical) properties. Significant spatial patterns were found in all three stands. PF stand had the greatest coarse scale spatial patterns (> 23 m) and availability of major macronutrients (N, P, and K). Shorter spatial range (5 to 10 m) of nutrient availability was observed in the stand with longest time since fire. Soil microbial activity was the strongest driver of nutrient availability in the PF stand, whereas contributions from aboveground variables such as understory vegetation, tree canopy cover, coarse woody debris (CWD), distance to nearest tree, and tree size was observed only in the CC and MA stands. The findings from the current study suggest that post-fire nutrient availability follows spatially predictable patterns, and confirm the hypothesis that stand replacing fire creates uniformity in nutrient availability and that the development of post-fire heterogeneity is a product of increasing ecosystem complexity.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".