Environmental control and spatial structures in peatland vegetation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it