Reduced-Impact Logging Maintain High Moss Diversity in Temperate Forests
Bibliographic record
Abstract
Forestry harvesting represents an important economic activity around the world. Habitat degradation due to forest harvesting contributes to biodiversity loss; therefore, it is necessary to implement logging management aimed at reducing its impact. Forest management by reduce-impact logging (RIL) involves cutting trees following regulations focused on diminishing the impact on biodiversity by following harvesting plans based on forestry inventories and participation of trained workers. In Mexico, RIL is applied mainly in temperate habitats and its effectiveness has been assessed based on vascular plants. In this study, we analyzed the diversity and community structure of terrestrial and epiphytic mosses in managed (sites number = 3) and conserved (sites number = 3) sites in the temperate forest of Sierra Juárez, Oaxaca, Mexico. Likewise, we evaluated the potential function of mosses as indicators of habitat degradation. Environmental variables were also quantified at local (canopy coverage, altitude, daily temperature, and light) and regional (total annual rainfall, orientation, and slope) scales to evaluate potential relationships with the community and species diversity. We documented 70 mosses species with a diversity (alfa, beta) and community structure similar between managed and conserved sites. For terrestrial mosses, we found marginal differences in their communities, likely related to species coverture variation in managed sites. The diversity and community structure epiphytic mosses were not statistically different in managed and conserved sites. Only the daily variation in light intensity was positively related to the variation of alpha diversity of epiphytic mosses. The species Dicranum sumichrastii Duby and Leptodontium viticulosoides (P. Beauv.) Wijk & Margad. can be considered as ecological indicators for conserved and managed sites, respectively, likely due to their relationship with light and humidity conditions. Our results suggest that that forest management by RIL could be considered as a promising tool to balance timber production and moss diversity.
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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 | 0.000 | 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 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".