Can bryophyte groups increase functional resolution in tundra ecosystems?
Bibliographic record
Abstract
The relative contribution of bryophytes to plant diversity, primary productivity, and ecosystem functioning increases towards colder climates. Bryophytes respond to environmental changes at the species level, but because bryophyte species are relatively difficult to identify, they are often lumped into one functional group. Consequently, bryophyte function remains poorly resolved. Here, we explore how higher resolution of bryophyte functional diversity can be encouraged and implemented in tundra ecological studies. We briefly review previous bryophyte functional classifications and the roles of bryophytes in tundra ecosystems and their susceptibility to environmental change. Based on shoot morphology and colony organization, we then propose twelve easily distinguishable bryophyte functional groups. To illustrate how bryophyte functional groups can help elucidate variation in bryophyte effects and responses, we compiled existing data on water holding capacity, a key bryophyte trait. Although plant functional groups can mask potentially high interspecific and intraspecific variability, we found better separation of bryophyte functional group means compared with previous grouping systems regarding water holding capacity. This suggests that our bryophyte functional groups truly represent variation in the functional roles of bryophytes in tundra ecosystems. Lastly, we provide recommendations to improve the monitoring of bryophyte community changes in tundra study sites.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".