BRYOPHYTE RESPONSES TO MICROCLIMATIC EDGE EFFECTS ACROSS RIPARIAN BUFFERS
Bibliographic record
Abstract
Although riparian buffers are an important aspect of forest management in the boreal forest of Canada, little is known about the habitat conditions within buffers, due in part to complex edge effects in response to both the upland clearcut and the stream. We investigated microclimatic conditions and bryophyte growth and vitality in seven locations between the stream edge and 60 m into the upland undisturbed conifer forests and at the clearcut sites with riparian buffer 30 km northwest of Thunder Bay, Ontario, Canada. We hypothesized that the growth and vitality of a pleurocarpous moss, Hylocomium splendens, and an acrocarpous moss, Polytrichum commune, would be directly related to the microclimatic gradients detected. We further hypothesized that sensitivity of the bryophytes to environmental factors will vary depending on their life form type, i.e., pleurocarpous moss will respond differently than the acrocarpous moss. Both bryophyte species were transplanted in pots and placed at 10-m intervals along 60-m transects perpendicular to the stream across the buffer and undisturbed sites. Bryophyte growth, cover, and vitality, as well as microclimatic parameters and plant cover, were measured over the summer in 2003. The riparian buffers were simultaneously affected by microclimatic gradients extending from both the clearcut edge and the riparian-upland ecotonal edge. Both bryophyte species responded to changes in the microclimatic conditions. However, vapor pressure deficit (VPD) was the most important factor influencing the growth of H. splendens, whereas for P. commune growth soil moisture was most important. Our study confirms earlier findings that interior forest bryophytes such as H. splendens can be used as indicators to monitor edge effects and biodiversity recovery following forest harvesting. We demonstrate that growth and vitality of these bryophytes reflect the prevailing near-ground microclimatic conditions at the forest edges. Abundance estimates of such bryophytes can be used to determine the depth of edge effects across both ecotonal edges (e.g., riparian-upland forest edge) and anthropogenically created edges (e.g., clearcut edge). Forest management practices must consider depth of edge in determining the appropriate width of riparian buffers that would be necessary to sustain biodiversity and associated values at the land/water interface.
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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) | 0.001 | 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".