The ecohydrology of forested peatlands: Simulating the effects of tree shading on moss evaporation and species composition
Bibliographic record
Abstract
Abstract Forested peatlands represent an important global carbon pool, storing 48.0 Pg of carbon within continental western Canada alone. Peatland hydrology regulates the carbon dynamics and future stability of this carbon store and provides a critical control on regional water dynamics. Drying associated with land‐use change and climate change has the potential to increase tree growth, modifying the density, size, and spatial arrangement of trees. This can reduce peatland evaporation and offset the associated increase in transpiration. To determine the magnitude of this negative ecohydrological feedback, we simulated spatial variations in radiation, turbulent energy fluxes, and temperatures in peatlands with real and idealized tree densities and distributions. For a random tree distribution, an increase in tree density from 0 to 4 trees per m2 reduced available energy at the peat surface, decreasing average evaporation by 25%. At higher tree densities, feather moss species covered a larger fraction of the ground because of lower light availability. In combination with the lower energy availability, this change in moss composition reduced evaporation by ~70%. The reduction in evaporation was greater (83%) when the effects of increased canopy cover on peatland aerodynamic properties were incorporated. Additionally, we found that evaporation was dependent on the spatial arrangement of trees, with evaporation being higher when trees were clustered. Overall, our model showed that the trade‐off between reduced evaporation and increased transpiration with increasing tree densities reduced landscape variation in evapotranspiration, with simulated evapotranspiration remaining approximately constant across a broad range of peatland ecosystems despite varying canopy densities.
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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 | 0.001 |
| 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) | 0.001 | 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".