Forest stand complexity controls ecosystem‐scale evapotranspiration dynamics: Implications for landscape flux simulations
Bibliographic record
Abstract
Abstract Open‐canopy forested systems are found across a range of terrestrial biomes. Forest structure and organization in open‐canopy systems exhibit substantial controls on system process dynamics such as evapotranspiration (ET). The energy reaching sub‐canopy forest layers is greater in open‐canopy systems compared to closed canopy systems, with high spatiotemporal variability in the distribution of energy that both drives ET and controls sub canopy species composition and organization. Yet the impact of their structural complexity and organization on whole system ET dynamics is poorly understood. Using the BETA+ model and measured eddy covariance‐based ET fluxes from a boreal treed peatland, we critically evaluate how stand compositional and organizational complexity influences ET dynamics. Model simulations iteratively increase complexity from a simple ‘big‐leaf’ model to a model representing spatial complexity of all system layers, demonstrating the effect of each complex system component on stand ET dynamics. We show that including forest stand complexity and associated canopy and radiation variability increases ET model estimates by ~26%. In addition to changes in the ET estimates, the inclusion of this spatial complexity is shown to induce temporal variations in the simulated ET that improves model performance by reducing unexplained variance between modelled and measured ET by 10% and reducing hysteresis in model results. These results have clear implications for flux modelling of forest systems and for larger scale climate models where open canopy systems such as this dominate the landscape. Demonstrating that whilst big leaf simulation can approximate ET fluxes, the inclusion of forest‐stand complexity and its influence on spatiotemporal radiation fluxes and ecohydrological processes are necessary to effectively represent ET dynamics within open canopies.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".