Environmental control and spatial structures in peatland vegetation
Bibliographic record
Abstract
Question: What are the relative influences of environment and space in structuring the plant composition in a peatland complex? Location: Lakkasuo, southern boreal zone, Finland. Method: We used principal coordinates of neighbour matrices (PCNM) to model spatial structures in the plant composition of a peatland complex comprising ombrotrophic and minerotrophic, open and forested areas. We used redundancy analyses (RDA) and variation partitioning to assess the relative influences of chemical variables (peat and water characteristics), physical variables (hydrology, soil properties, shade), as well as broad-scale (>350 m) and medium-scale (100–350 m) spatial structures on vegetation assemblages. Results: We identified five different significant spatial patterns circumscribing (1) the minerotrophic–ombrotrophic gradient; (2) dry ombrotrophic and wet minerotrophic areas; (3) open and shaded areas; (4) dry open/shaded and wet patches within the ombrotrophic areas; and (5) dry open patches and dry forested patches. With spatial structures and environmental variables, we were able to model 30% of the variability in plant composition in the peatland complex, 13% of which was attributable to spatial structures alone. Conclusions: We demonstrated that in the peatland complex, the spatial dependence processes were more important at the broadest scale, and found that patterns at a medium scale might reflect finer-scale patterns that were not investigated here. Spatial autocorrelation in vegetation composition in the peatland complex appeared to be driven by Sphagnum species. Our results emphasize that spatial modelling should be routinely implemented in studies looking at species composition, since they significantly increase the explained proportion of variance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".