Changes in forest floor bryophyte (moss and liverwort) communities 4 years after forest harvest
Bibliographic record
Abstract
Forest harvest presents a potential threat to forest floor bryophyte communities primarily through alteration of the microclimate and disturbance of substrates on the forest floor. Management, including harvest, applied at the landscape scale creates patches of disturbance of differing severities at the spatial scale experienced by bryophytes. Presumably, bryophyte diversity in managed landscapes is best conserved by forest harvest techniques that minimize community change, thereby allowing disturbed communities to reassemble to approach predisturbance composition. We monitored bryophyte community reassembly by sampling quadrats established in a 54-ha management block of Acadian forest in New Brunswick, before and after harvest. Quadrats were either in unharvested areas, or experienced a range of disturbance severities from removal of some or all canopy, to forest floor disturbance with complete canopy removal. Bryophyte communities showed compositional change over 4 years, even in areas that were not harvested. Although species richness was maintained or recovered 4 years after harvest, changes in species composition were significant in all disturbance classes with greatest change related to forest floor disturbance. In particular, liverworts were lost in areas with forest floor disturbance. We suggest that the simplest method to reduce immediate species loss, and presumably promote conservation of bryophyte communities within managed forest landscapes, is to utilize techniques that reduce the area of forest floor and associated substrates that are physically disrupted.Key words: bryophyte, community change, disturbance, forest harvest, monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".