The Impact of Seasonal and Annual Climate Variations on the Carbon Uptake Capacity of a Deciduous Forest Within the Great Lakes Region of Canada
Bibliographic record
Abstract
Abstract In eastern North America, many deciduous forest ecosystems grow at the northernmost extent of their geographical ranges, where climate change could aid or impede their growth. This region experiences frequent extreme weather conditions, allowing us to study the response of these forests to environmental conditions, reflective of future climates. Here we determined the impact of seasonal and annual climate variations and extreme weather events on the carbon (C) uptake capacity of an oak‐dominated forest in southern Ontario, Canada, from 2012 to 2016. We found that changes in meteorology during late May to mid‐July were key in determining the C sink strength of the forest, impacting the seasonal and annual variability of net ecosystem productivity (NEP). Overall, higher temperatures and dry conditions reduced ecosystem respiration (RE) much more than gross ecosystem productivity (GEP), leading to higher NEP. Variability in NEP was primarily driven by changes in RE, rather than GEP. The mean annual GEP, RE, and NEP values at our site during the study were 1,343 ± 85, 1,171 ± 139, and 206 ± 92 g C m−2 yr−1, respectively. The forest was a C sink even in years that experienced heat and water stresses. Mean annual NEP at our site was within the range of NEP (69–459 g C m−2 yr−1) observed in similar North American forests from 2012 to 2016. The growth and C sequestration capabilities of our oak‐dominated forest were not adversely impacted by changes in environmental conditions and extreme weather events experienced over the study period.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".