Spatial pattern of intraspecific trait variability in <i>Sphagnum fuscum</i>
Bibliographic record
Abstract
Recent studies have shown that intraspecific variability is a mechanism by which species respond to environmental heterogeneity, and that intraspecific variation can have large implications for ecological processes. Here, we studied whether there is meaningful intraspecific variation in the ecohydrological traits, biomass allocation, and decomposability in Sphagnum moss, and if so, to explore the spatial pattern of variability. We implemented a hierarchical design in which we quantified traits of S. fuscum at three spatial scales: (i) between individuals within 8 cm2 patches; (ii) between replicate patches located within a single hummock or hollow location; and (iii) between hummocks. Although we focused on S. fuscum, we also compared the variability in some morphological features of S. fuscum and S. magellanicum. If growth is affected by density, we expected variability to be lowest at the patch level. Contrary to our expectation, most of the variability in both species occurred within-patch, which is our smallest sampling unit. Variability was generally higher in the traits for S. magellanicum compared with the variability in the traits for S. fuscum, which was generally negligible. Also, the pattern of variability observed for some of the traits such as the capitulum mass suggests that the mechanisms controlling different traits may be operating at different spatial scales.
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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 | 0.001 | 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".