Investigating Carbon Dynamics of a Young Temperate Coniferous Forest Using Long-Term Eddy Covariance Flux Observations
Bibliographic record
Abstract
Plantation and managed forests are major sink of atmospheric CO2 in North America and across the world. If properly managed, these forests may help to offset anthropogenic greenhouse gas emissions to mitigate climate change. This study investigated the impacts of climate variability, extreme weather events, and disturbance (thinning) on the growth and carbon (C) exchanges of a young temperate coniferous plantation forest (48-year-old white pine (Pinus strobus)) in the Great Lakes region in Canada using long-term eddy covariance flux observations. CO2 fluxes, as well as meteorological and soil variables were continuously measured from 2008 to 2021 (14 years) to estimate net ecosystem productivity (NEP), ecosystem respiration (RE), and gross ecosystem productivity (GEP). Soil respiration (Rs) was also measured using automatic soil chambers from 2017 to 2019. Selective thinning was conducted first time in this stand in January 2021 to remove approximately 1/3 of the basal area. Study results showed that climate conditions in the early growing season, from late May to mid-July, determined the overall strength of C uptake in any given year. However, above-average temperature and precipitation in the late growing season significantly reduced NEP and even in some cases, transformed the forest into a net C source for short periods due to large pulses of RE. Mean annual GEP, RE and NEP values were 1660 ±199, 1087 ±96 and 592 ±169 g C m-2 yr-1, respectively, from 2008 to 2021. Thinning did not significantly impact the C uptake of the forest as the stand remained a net C sink with an annual NEP of 648 g C m-2 yr-1 in 2021. Changes in annual GEP, RE and NEP in 2021 remained within the range of interannual variability over the study period. Overall, Rs accounted for roughly 89% of the annual RE in this stand. A complete understanding of the response of forest C dynamics to climate variability and thinning in young plantation forests is critical to guiding future forest management efforts for enhancing the growth and C uptake of these forest plantations to maximize their potential in support of providing nature-based climate solutions.
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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 | 0.000 | 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 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".