FOREST PRODUCTIVITY DECLINE CAUSED BY SUCCESSIONAL PALUDIFICATION OF BOREAL SOILS
Bibliographic record
Abstract
Long-term forest productivity decline in boreal forests has been extensively studied in the last decades, yet its causes are still unclear. Soil conditions associated with soil organic matter accumulation are thought to be responsible for site productivity decline. The objectives of this study were to determine if paludification of boreal soils resulted in reduced forest productivity, and to identify changes in the physical and chemical properties of soils associated with reduction in productivity. We used a chronosequence of 23 black spruce stands ranging in postfire age from 50 to 2350 years and calculated three different stand productivity indices, including site index. We assessed changes in forest productivity with time using two complementary approaches: (1) by comparing productivity among the chronosequence stands and (2) by comparing the productivity of successive cohorts of trees within the same stands to determine the influence of time independently of other site factors. Charcoal stratigraphy indicates that the forest stands differ in their fire history and originated either from high- or low-severity soil burns. Both chronosequence and cohort approaches demonstrate declines in black spruce productivity of 50-80% with increased paludification, particularly during the first centuries after fire. Paludification alters bryophyte abundance and succession, increases soil moisture, reduces soil temperature and nutrient availability, and alters the vertical distribution of roots. Low-severity soil burns significantly accelerate rates of paludification and productivity decline compared with high-severity fires and ultimately reduce nutrient content in black spruce needles. The two combined approaches indicate that paludification can be driven by forest succession only, independently of site factors such as position on slope. This successional paludification contrasts with edaphic paludification, where topography and drainage primarily control the extent and rate of paludification. At the landscape scale, the fire regime (frequency and severity) controls paludification and forest productivity through its effect on soil organic layers. Implications for global carbon budgets and sustainable forestry are discussed.
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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 | 0.001 |
| 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".