Effects of long-term fertilization of forest soils on potential nitrification and on the abundance and community structure of ammonia oxidizers and nitrite oxidizers
Bibliographic record
Abstract
Forest fertilization in British Columbia is increasing, to alleviate timber shortfalls resulting from the mountain pine beetle epidemic. However, fertilization effects on soil microbial communities, and consequently ecosystem processes, are poorly understood. Fertilization has contrasting effects on ammonia-oxidizing bacteria and archaea (AOB and AOA) in grassland and agricultural ecosystems, but there are no studies on AOB and AOA in forests. We assessed the effect of periodic (6-yearly application 200 kg N ha⁻¹) and annual (c. 75 kg N ha⁻¹) fertilization of lodgepole pine and spruce stands at five long-term maximum productivity sites on potential nitrification (PN), and the abundance and diversity of AOB, AOA and Nitrobacter and Nitrospira-like nitrite-oxidizing bacteria (NOB). Fertilization increased AOB and Nitrobacter-like NOB abundances at some sites, but did not influence AOA and Nitrospira-like NOB abundances. AOB and Nitrobacter-like NOB abundances were correlated with PN and soil nitrate concentration; no such correlations were observed for AOA and Nitrospira-like NOB. Autotrophic nitrification dominated (55–97%) in these forests and PN rates were enhanced for up to 2 years following periodic fertilization. More changes in community composition between control and fertilized plots were observed for AOB and Nitrobacter-like NOB than AOA. We conclude that fertilization causes rapid shifts in the structure of AOB and Nitrobacter-like NOB communities that dominate nitrification in these forests.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".