Long‐term community change: bryophytes are more responsive than vascular plants to nitrogen deposition and warming
Bibliographic record
Abstract
Abstract Aims Many studies of vegetation change over multiple decades have focused on vascular plants, but very few on bryophytes, despite the importance of bryophytes for overall plant biodiversity and ecosystem functioning. Using a repeated survey of vascular plants and bryophytes in a forest ecosystem, we tested predictions of the hypotheses that: (1) vegetation change has been driven by N deposition and climate warming, and (2) bryophytes are more responsive to environmental change than vascular plants. Location Lowland temperate forest, northwest France. Methods In forest plots initially surveyed in 1976, we re‐surveyed both vascular plants and bryophytes in 2009 and 2012, respectively. We analysed changes in α‐diversity, β‐diversity, and species composition, and we used community‐weighted mean values of species affinities for temperature, light, pH, soil moisture and N to assess the temporal responses potentially caused by warming, N deposition, or possibly a changing light regime. Results We observed significantly increased species richness of bryophytes and decreased richness of vascular plants. Community affinities to N, pH and temperature increased significantly for bryophytes, but not for vascular plants, although the change over time in N affinities for vascular plants was qualitatively in the predicted direction. Bryophytes showed a higher magnitude of temporal community change than vascular plants, both in terms of overall species composition and environmental affinities, indicating a higher responsiveness of bryophytes to environmental change. Conclusion Overall, the result of more marked temporal community change for bryophytes suggests that the many studies of changes in vascular plant communities over time might underestimate the sensitivity of the broader plant community (including cryptogams) to environmental change.
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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 | 0.001 | 0.001 |
| 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".