Environmental controls of Picea mariana water use in a boreal subarctic peatland
Bibliographic record
Abstract
Black spruce (Picea mariana) dominated peat plateaus are an important component of northwestern Canada’s heterogeneousboreal landscape. Threats to these ecosystems, including permafrost thaw and wetland expansion, could impact hydrological fluxes therefore, it is essential to understand the factors affecting the hydraulic function of black spruce in these rapidly changing landscapes. Sap velocity (Vs, cm·hr−1) is the movement of water and minerals through tree stems during the growth period and can be used as an indicator for plant water use and the quantification of tree transpiration. Here, we identified the meteorological variables driving daytime and nighttime Vs in Picea mariana (black spruce) trees growing across a 21 hectare (20 m2 grid) subarctic boreal peatland complex underlain with discontinuous permafrost, ~630 km west of Yellowknife, Northwest Territories (61°18'N, 121°18'W; ForestGEO Plot). For two consecutive growing seasons (2017 and 2018), eighteen black spruce trees were instrumented with sap flow sensors using the heat-ratio method to measure Vs. Meteorological variables including vapor pressure deficit (VPD) and photosynthetically active radiation (PAR) accounted for 57 and 73% of the variance in daytime mean Vs in 2017 and 2018, respectively, while VPD, PAR and air temperature accounted for 26 and 40% of Vs variance at night. VPD and PAR were the strongest meteorological drivers of black spruce Vs in the ForestGEO Plot. An increase in either variable corresponded to an increase in Vs across various time periods (day/nighttime). In addition, we investigated how daytime seasonal mean/maximum Vs for black spruce was affected by local environmental factors including fibric layer depth, organic matter decomposition, black spruce density, black spruce basal area, phosphorus supply rate (P) and soil water content (SWC) when physiological traits of black spruce, including diameter at breast height and crown area, were considered as covariables. It was hypothesised that stand density and basal area would affect Vs, but results indicated that only P and SWC had a (weak) influence on black spruce Vs. The variables P and SWC had a greater influence on the amplitude (seasonal daily maximum) of Vs over the sampling period. Overstory vegetation in Canada’s Northwestern boreal forest is important for the terrestrial water cycle through tree water storage, and transpiration, therefore the quantification of black spruce transpiration and an improved understanding of the environmental controls of black spruce Vs in boreal peatlands would be a natural next step for this research.
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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 | 0.000 |
| 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 | 0.000 |
| 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 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".