How do natural disturbances and human activities affect soils and tree nutrition and growth in the Canadian boreal forest?
Bibliographic record
Abstract
There are concerns about the effect of increasing resource extraction and other human activities on the soils and vegetation of the boreal zone. The review covers published papers between 1974 and 2012 to assess the effects of natural disturbances and human activities on soils and tree nutrition and growth of the Canadian boreal zone. Changes in soil and foliar nutrients following disturbance were also analyzed by meta-analysis. When sufficient replicated studies were not available for a given disturbance or nutrient, response assessments or narrative summaries are presented. The majority of fertilization studies in the boreal zone showed a positive tree growth response to nitrogen (N) and phosphorus (P) fertilization either individually or in combination. Large amounts of N may be lost through volatilization following fire depending on the severity and frequency of the fire. This may contribute to N limitation in the boreal zone. Available soil P and extractable calcium (Ca) and magnesium (Mg) increased in the surface horizons following fire. In contrast, extractable P decreased following harvest. Harvesting had no effect on total or inorganic N except in mixedwoods where total N decreased in the surface organic horizon following harvest. These are potential areas of concern given tree growth responses to N and P fertilization. Potassium (K) in the forest floor did not change following fire or harvesting; thus, K availability for tree nutrition should not be at risk, since its cycle is rapidly restored. Mercury (Hg) cycling may be altered in the boreal zone as a result of flooding and if fire return intervals and intensities increase. Interactions of multiple disturbances may increase the risk of nutrient depletions, but there is currently little information on these interactions in the boreal zone. Evidence to date suggests the soils of the Canadian boreal zone have not been adversely affected except in localized areas. However, there is the risk of nutrient loss if soils are not considered in our forest management strategies, particularly where multiple disturbances may interact. The potential for off-site movement of nutrients and contaminants into the atmospheric and aquatic ecosystems, in addition to on-site environmental issues, is also a concern.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".