Modelling <i>Sphagnum</i> moisture stress in response to projected 21st‐century climate change
Bibliographic record
Abstract
Abstract Sphagnum moss is an important genus of plants responsible for large stores of soil organic carbon associated with wet habitats, such as northern peatlands. Northern peatlands, which contain globally significant quantities of legacy carbon, may be vulnerable to enhanced summer moisture deficits due to climate change. We adapted HYDRUS‐1D and a semi‐arid soil‐moisture model to investigate the role of microtopographic position and depth dependence of peat hydraulic properties on Sphagnum moisture‐stress response to current and projected climate conditions in an idealized northern Michigan peatland. Water table (WT) level was shown to have a strong control on pore‐water pressure ( ψ ) and thus on Sphagnum moisture stress. As a result of the close correspondence between surface peat hydraulic properties for hummocks and hollows used to parameterize our model, the microtopographic position was shown to have a greater impact on Sphagnum moisture stress. Model behaviour suggests that, while ψ maintains equilibrium‐profile values relative to the WT level for relatively shallow values, surface ψ becomes nonlinearly related to the WT level below a value of approximately −0.4 m, thus, greatly increasing the likelihood of desiccation under future climate scenarios, where growing‐season soil‐moisture deficits are projected to increase. The simulated range of instantaneous and cumulative moisture stress for hollows under future climate conditions closely corresponds to the contemporary range exhibited by hummocks. Therefore, in order to assess the competitive advantage of various Sphagnum species to future climate conditions, we argue that more data are needed to better inform a physiological ψ ‐based moisture‐stress threshold, the evolution of the stress response to increasing levels of desiccation and its subsequent recovery dynamics. Copyright © 2015 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".