Depth‐to‐water mediates bryophyte response to harvesting in boreal forests
Bibliographic record
Abstract
Abstract Site moisture is an important component of the forest landscape for maintaining biodiversity, including forest‐floor bryophytes. However, little is known about its role in shaping understorey responses to harvesting. We investigated the influence of site wetness, determined using a remotely sensed, topographic depth‐to‐water (DTW) index, on responses of bryophyte cover, richness, diversity and composition to variable retention harvesting (comparing: 2% [clear‐cut], 20% and 50% dispersed green tree retention and uncut controls [100% retention]) in three boreal forest cover‐types (broadleaf, mixed and conifer forests) in western Canada. The DTW index provides an approximation of DTW at or below the soil surface and was derived from wet‐areas mapping based on discrete Airborne Laser Scanning data acquired over an experimentally harvested landscape located in north‐western Alberta, Canada. The effectiveness of leaving retention (vs. clear‐cutting) for conserving bryophyte communities depended on site wetness, as indicated by DTW, with the specifics varying among forest types. In broadleaf forests, bryophyte cover and richness were generally low and not much affected by harvesting but drier sites had higher richness and a few more unique species. In mixed and conifer forests, leaving retention (vs. clear‐cutting) on wetter (vs. drier) sites was more effective for conserving bryophyte cover, wetter sites had higher total species richness and more species were exclusive to wetter sites. Synthesis and applications. Site wetness, as indicated using the remotely sensed topographic site wetness index “depth‐to‐water” mediates bryophyte responses to variable‐retention harvests. Specifically, our results suggested that in conifer and mixed forests it would be more beneficial to target wetter sites for retention patches or dispersed retention whereas in broadleaf forests there might be a slight advantage to targeting drier sites. Our study demonstrates that this tool could be used to inform management decisions around leaving dispersed or patch retention.
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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".