Effects of leaf litter and environment on bryophytes in boreal forests of Alaska
Bibliographic record
Abstract
Bryophytes dominate the forest floor, regulate soil microclimate, and participate in the carbon (C) and nitrogen (N) cycles in coniferous boreal forests, while deciduous stands support a lower bryophyte abundance. Recent increases in fire severity throughout the northwestern boreal forest could result in a shift from conifer to deciduous dominated stands. Although broadleaf litter is hypothesized to be detrimental to bryophytes, little is known regarding the mechanisms through which forest canopies affect moss communities and ecosystem processes. In this study, I explored how canopy type and leaf litter affect bryophyte succession, growth, and associated N2-fixation in interior Alaska. I quantified bryophyte communities in a 163-year chronosequence in deciduous-broadleaf and coniferous stands. Bryophyte communities diverged at about 40 years after fire, with low bryophyte cover in mature deciduous forests and high bryophyte cover in mature coniferous forests, which was associated with increased leaf litter in deciduous stands. I then directly tested leaf litter as the main mechanism explaining this discrepancy in bryophyte abundance between the two forest types using a field-based experiment. I used transplants of a common bryophyte, the feather moss Hylocomium splendens (Hewd) Schimp., in deciduous and coniferous stands with manipulations of broadleaf litter to quantify the impacts of leaf litter on moss growth. I found that leaf litter had a much larger detrimental impact on moss growth than the other environmental characteristics of deciduous and coniferous stands, therefore providing experimental support to this previously hypothesized mechanism. Lastly, feather mosses are known to host N2-fixing cyanobacteria, but little is known regarding how this ecosystem process varies temporally, spatially, or among forest types. I measured N2-fixation rates in feather mosses over three years using stable isotope labelling (15N2). I found that N2-fixation rates were extremely variable among years and were linked to precipitation. Feather moss contribution to ecosystem N-budgets remained low in deciduous stands, while they were much higher and variable in coniferous stands. Changes in canopy composition and associated changes in leaf litter inputs induced by climate change will have profound impacts on moss communities, and may have cascading impacts on forest structure and composition.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".