Retention of tree-related microhabitats is more dependent on selection of habitat trees than their spatial distribution
Bibliographic record
Abstract
Abstract Habitat trees, which provide roosting, foraging and nesting for multiple taxa, are retained in managed forests to support biodiversity conservation. To what extent their spatial distribution influences provisioning of habitats has rarely been addressed. In this study, we investigated whether abundance and richness of tree-related microhabitats (TreMs) differ between habitat trees in clumped and dispersed distributions and whether the abundance of fifteen groups of TreMs is related to tree distribution patterns. To identify habitat trees, we quantified TreMs in temperate mountain forests of Germany. We determined clumping (the Clark–Evans index), size of the convex hull, diameter at breast height, as well as altitude, slope and aspect of sites for their possible influence on TreMs. We additionally determined the difference in TreM abundance and richness among four options of selecting five habitat trees per ha from 15 candidates: (a) the most clumped trees, (b) five randomly selected and dispersed trees, (c) the single tree with highest abundance or richness of TreMs and its four closest neighbors and (d) a “reference selection” of five trees with known highest abundance or richness of TreMs irrespective of their distribution. The degree of clumping and the size of the convex hull influenced neither the abundance nor richness of TreMs. The reference selection, option (d), contained more than twice the number of TreMs compared to the most clumped, (a), or random distributions, (b), of five habitat trees, while option (c) assumed an intermediate position. If the goal of habitat tree retention is to maximize stand-level abundance and richness of TreMs, then it is clearly more important to select habitat trees irrespective of their spatial pattern.
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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 | 0.000 | 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) | 0.001 | 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".