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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".