Size-mediated tree transpiration along soil drainage gradients in a boreal black spruce forest wildfire chronosequence
Bibliographic record
Abstract
Boreal forests are crucial to climate change predictions because of their large land area and ability to sequester and store carbon, which is controlled by water availability. Heterogeneity of these forests is predicted to increase with climate change through more frequent wildfires, warmer, longer growing seasons and potential drainage of forested wetlands. This study aims at quantifying controls over tree transpiration with drainage condition, stand age and species in a central Canadian black spruce boreal forest. Heat dissipation sensors were installed in 2007 and data were collected through 2008 on 118 trees (69 Picea mariana (Mill.) Britton, Sterns & Poggenb. (black spruce), 25 Populus tremuloides Michx. (trembling aspen), 19 Pinus banksiana Lamb. (jack pine), 3 Larix laricina (Du Roi) K. Koch (tamarack) and 2 Salix spp. (willow)) at four stand ages (18, 43, 77 and 157 years old) each containing a well- and poorly-drained stand. Transpiration estimates from sap flux were expressed per unit xylem area, J(S), per unit ground area, E(C) and per unit leaf area, E(L), using sapwood (A(S)) and leaf (A(L)) area calculated from stand- and species-specific allometry. Soil drainage differences in transpiration were variable; only the 43- and 157-year-old poorly-drained stands had ∼ 50% higher total stand E(C) than well-drained locations. Total stand E(C) tended to decrease with stand age after an initial increase between the 18- and 43-year-old stands. Soil drainage differences in transpiration were controlled primarily by short-term physiological drivers such as vapor pressure deficit and soil moisture whereas stand age differences were controlled by successional species shifts and changes in tree size (i.e., A(S)). Future predictions of boreal climate change must include stand age, species and soil drainage heterogeneity to avoid biased estimates of forest water loss and latent energy exchanges.
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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 | 0.000 |
| 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".