Modelling the habitat preference of two key <i>Sphagnum</i> species in a poor fen as controlled by capitulum water retention
Bibliographic record
Abstract
Abstract. Current peatland models generally lack dynamic feedback between the plant community structure and the environment, although the vegetation dynamics and ecosystem functioning are tightly linked. Realistic projections of peatland response to climate change requires including vegetation dynamics in ecosystem models. In peatlands, Sphagnum mosses are key engineers. The species composition in a moss community varies primarily following habitat moisture conditions. Hence, modelling the mechanisms in controlling the habitat preference of Sphagna is a good first step for modelling the community dynamics in peatlands. In this study, we developed the Peatland Moss Simulator (PMS), a process-based model, for simulating community dynamics of the peatland moss layer that results in habitat preferences of Sphagnum species along moisture gradients. PMS employed an individual-based approach to describe the variation of functional traits among shoots and the stochastic base of competition. At the shoot-level, growth and competition were driven by net photosynthesis, which was regulated by hydrological processes via capitulum water retention. The model was tested by predicting the habitat preferences of S. magellanicum and S. fallax, two key species representing dry (hummock) and wet (lawn) habitats in a poor fen peatland (Lakkasuo, Finland). PMS successfully captured the habitat preferences of the two Sphagnum species, based on observed variations in trait properties. Our model simulation further showed that the validity of PMS depended on the interspecific differences in capitulum water retention being correctly specified. Neglecting the water-retention differences led to the failure of PMS to predict the habitat preferences of the species in stochastic simulations. Our work highlights the importance of capitulum water retention to the dynamics and carbon functioning of Sphagnum communities in peatland ecosystems. Studies of peatland responses to changing environmental conditions thus need to include capitulum water processes as a control on the vegetation dynamics. For that our PMS model could be used as an elemental design for the future development of dynamic vegetation models for peatland ecosystems.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".