Invertebrate Diversity under Artificial Cover in Relation to Boreal Forest Habitat Characteristics
Bibliographic record
Abstract
We investigated invertebrate diversity in boreal forests using an experimental design that consisted of counting soil invertebrates under artificial cover. The aim was to assess the utility of using soil invertebrate diversity as a measure of ecosystem health. The study area was grouped into five habitats: upland hardwood, lowland hardwood, conifer, shrub, and conifergrass. Simpson’s and Shannon’s indices of invertebrate diversity were negatively correlated with percent herbaceous cover. Number of recognizable taxonomic units (RTU richness) was negatively correlated with percent litter cover. The number of individual invertebrates was positively correlated with soil moisture and negatively correlated with percent conifer cover. Invertebrate diversity varied among habitat types, with conifer forests (spruce, fir, pine) having the highest diversity and regenerating conifer-grass forests having the lowest diversity, suggesting that successional stages affect diversity. The most productive sites, upland and lowland hardwood habitats, had the highest abundance of soil invertebrates, although intermediate diversity compared to the other five habitats. The results are consistent with the view that diversity increases and then decreases with productivity and disturbance over succession (ca. 50-100 yr). Hence, maintenance of soil invertebrate diversity in managed boreal forests requires the provision of a varied landscape with a mosaic of disturbance regimes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".