Quantifying Peatland Ecohydrological Resilience to Drought and Wildfire by Thinking Outside the Bog 
Bibliographic record
Abstract
Peatlands are globally important long-term sinks of carbon, however there is concern that climate change-mediated drought will weaken their carbon sink function due to enhanced decomposition and moss moisture stress. Furthermore, heightened drought will also increase peat combustion loss during wildfire leading to peatland degradation and a potential ecosystem regime shift. Despite research developments on ecohydrological tipping points in semi-arid ecosystems, research in peatlands on the wet end of the ecosystem continuum has been “bogged down” (pun fully intended) by the traditional conceptual models of peatland hydrology and ecology. The consequences of this thinking loom large, given that northern peatlands face increases in the severity, areal extent, and frequency of climate-mediated (e.g., wildfire, drought) and land-use (e.g., drainage, flooding, and mining) disturbances, placing the future integrity of these critical ecosystem services in jeopardy.In this presentation we explore the need for “thinking outside the bog” to quantify the ecohydrological tipping points to drought and wildfire. We argue that peatland ecohydrological resilience is a non-linear function of water storage dynamics and that water table data or peat moisture data alone are insufficient to capture this hydrological complexity. Given that the ability of Sphagnum moss to resist drought is largely a function of the rate of water loss by evaporation, the rate of upward water supply from the water table, and the water storage properties of the peat matrix, we suggest that ecohydrological resilience can be quantified by the magnitude and duration of the disconnect between the water table and near-surface peat. We discuss ways to measure ecohydrological resilience and explore simple metrics that reveal when critical tipping points have been exceeded and the implications this has for carbon storage and fluxes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".