Forest Fragmentation Slows the Decomposition of Coarse Woody Debris in a Subtropical Forest
Bibliographic record
Abstract
Abstract Forest fragmentation is increasing rapidly around the world, and edge effects caused by fragmented forests can influence ecosystem functions and ecological processes, including coarse woody debris (CWD) decomposition. Understanding the influencing mechanisms of edge effect on CWD decomposition is needed to assess the effects of forest fragmentation on C cycling and storage. We measured rates of mass loss of CWD of Cinnamomum camphora and Pinus taiwanensis over two years at two distances (0-5m vs. 60m) from a forest edge at two altitudes (215 and 1400 m a.s.l.), in a subtropical forest. In addition, we determined the microbial community of each CWD and the soil beneath via phospholipid fatty acids (PLFAs). Mass loss of CWD 60 m from the forest edge was > 15 % greater than that at the edge (0-5m). Mass loss was positively correlated with the abundance of microbial community and moisture content of the decaying CWD. Distance from edge explained 17.4% of the total variation of the microbial abundance in CWD. The results indicated that the reduced abundance of microbial communities and moisture content at forest edges reduced rates of decomposition of CWD. Long-term experiments with more tree species and more forest types are needed to assess the edge effect's generality.
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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 | 0.001 | 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".