Effects of stand, landscape, and spatial variables on bird communities in larch plantations and deciduous forests in central Japan
Bibliographic record
Abstract
We examined the effects of stand, landscape, and spatial variables on bird communities in deciduous forests and Japanese larch ( Larix leptolepis (Sieb & Zucc.) Gord.) plantations in a montane region of Nagano Prefecture, central Japan. We used plot-transect methods at 97 sites: 33 in winter 2003 and 18 sites were added in the 2004 breeding season in deciduous forests, and 32 in winter 2004 and 14 sites were added in the 2005 breeding season in larch plantations. Bird–environment relationships were explored using partial redundancy analysis and partial regression analysis. We used spatial variables derived from principal coordinates of neighbor matrices as explanatory variables to detect nonrandom spatial structure of bird communities. Variation in bird communities was mainly explained by stand and spatial variables for both forest types, whereas the effects of landscape variables were small. In plantations, important stand variables for both seasons included stand height, elevation, and the basal area of vines, whereas shrubs, dwarf bamboo ( Sasa senanensis (Franch. & Savat.) Rehd.), and larch snags were important in the breeding season. In deciduous forests, the most important stand variable for both seasons was the basal area of northern Japanese hemlock ( Tsuga diversifolia (Maxim.) Mast.) and Nikko fir ( Abies homolepis Sieb. & Zucc.) trees. Spatial variables showed that bird communities had large-scale (>10 km) spatial variation that could not be explained by stand or landscape variables.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".