Impacts of intraspecific variability and local adaptation on the ecophysiology of mosses: an example with <i>Sphagnum magellanicum</i>
Bibliographic record
Abstract
Abstract Background Bryophytes are a diverse plant group and are functionally different from vascular plants. Yet, plant ecology theories and hypotheses are often presented in an inclusive term. The trait-based approach to ecology is no exception; largely focusing on vascular plant traits and almost exclusively on interspecific traits. Currently, we lack information about the magnitude and the importance of intraspecific variability to the ecophysiology of bryophytes and how these might translate to local adaptation—a prerequisite for adaptive evolution. Method We used transplant and factorial experiments involving moisture and light to ask whether variability in traits between morphologically distinct individuals of Sphagnum magellanicum from habitat extremes was due to phenotypic plasticity or local adaptation and the implications for the ecophysiology of the species. Key Results We found that the factors that discriminated between the plant origins in the field did not translate to their ecophysiological functioning and the pattern of variability changed with the treatments, which suggests that the trait responses were due largely to phenotypic plasticity. The trait responses suggest that the need for mosses to grow in clumps where they maintain a uniform growth rate may have an overriding effect on responses to environmental heterogeneity, and therefore a constraint for local adaptation. Conclusion The circumstances under which local adaptation would be beneficial in this plant group is not clear. We conclude that extending the trait-based framework to mosses or making comparisons between mosses and vascular plants under any theoretical framework would only be meaningful to the extent that growth form and dispersal strategies are considered.
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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".