Changes in bird communities by planting non-native spruce in coastal birch forests of northern Norway
Bibliographic record
Abstract
Coastal birch forests in northern Norway have over the last 50 years gradually been replaced with non-native spruce plantations. To investigate possible changes in bird communities, we compared species richness and composition in six forest types (mature spruce plantations, ecotones, mixed forests, and three birch forest types) in two regions over two years. In the southern region, birch forests tended to host a higher number of species than spruce plantations, but species composition differed between forest types. The species composition in rich birch forests was particularly distinguished from spruce plantations, but also from the less productive birch habitats. Furthermore, there were dissimilarities in species richness and composition between regions. Ground nesters and species nesting on their northern margins occurred most frequently in the southern region, whereas cavity-nesters were most abundant in the northern region. Some species declined in abundance from 1998 to 1999, whereas three species shifted their main distribution from birch forests to mixed forests/ecotones. Possible factors underlying these results are discussed. We recommend that researchers and managers pay attention to habitat qualities within forest types (i.e., other than tree species composition in overstory) as well as to regional variations in species assemblages when addressing outcomes of management practices.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".