Abundance, diversity, and resilience of nematode assemblages in forest soilsThis article is one of a selection of papers published in the Special Forum on Towards Sustainable Forestry — The Living Soil: Soil Biodiversity and Ecosystem Function.
Bibliographic record
Abstract
Forest litter and soil may contain >10 × 10 6 individual nematodes·m –2 and, regionally, >400 species. Root-feeding nematodes may be pathogenic to young plants; microbial-feeding nematodes may increase turnover of the microbial pool; predacious and omnivorous nematodes represent higher trophic levels. The spatial distribution and abundance of nematode species in forests reflect soil type, soil fertility, climate, canopy and understorey plant species, litter depth, forest age, and management. Nematodes may be important in forest nurseries; they occur throughout the rooting depth of forest trees; hyphal-feeding species may influence mycorrhizae; and insect-vectored Bursaphelenchus species are a quarantine risk. Nematode populations interact with those of other soil animals (e.g., mites, tardigrades, enchytraeids, and protozoa). The diversity and abundance of the nematode assemblage make nematodes a useful indicator of soil condition and soil processes. Information available from forest systems suggests that, as long as physical disturbance is minimized and remaining trees or herb layer moderate the microclimate, logging and other forestry operations have only transitory effects on nematode populations. Extreme disturbance, such as bulldozing and slash-and-burn management, can significantly reduce nematode abundance and diversity. In contrast, management that enhances growth of understorey or herb layer can stimulate nematode populations. Each of these changes can be related to changes in food resource availability and environmental conditions, such as soil temperature and moisture. Although details of soil nematode contributions to nutrient processes in forest soils are sparse, that their populations are maintained through cycles of moderate management practices suggest that their beneficial contributions will also be maintained.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".