The relative importance of environmental factors on the interannual variability of carbon fluxes in the boreal forest
Bibliographic record
Abstract
As climate change will cause a more pronounced rise of air temperature in northern high latitudes than in other parts of the world, it is expected that the strength of the boreal forest carbon sink will be altered. To better understand and quantify these changes, we studied the influence of different environmental controls (e.g., air and soil temperatures, soil water content, photosynthetically active radiation, normalized difference vegetation index) on the timing of the start and end of the boreal forest growing season and the net carbon uptake period in Canada. The influence of these factors on the growing season carbon exchanges between the atmosphere and the boreal forest were also evaluated. There is a need to improve the understanding of the role of the length of the growing season and the net carbon uptake period on the strength of the boreal forest carbon sink, as an extension of these periods might not necessarily result in a stronger carbon sink if other environmental factors are not optimal for carbon sequestration or enhance respiration. Here, we used 31 site-years of observation over three Canadian boreal forest stands: Eastern, Northern and Southern Old Black Spruce in Québec, Manitoba and Saskatchewan, respectively. Redundancy analyses were used to highlight the environmental controls that correlate the most with the annual net ecosystem productivity and the start and end of the growing season and the net carbon uptake period. Preliminary results show that the timing at which the air temperature becomes positive correlates the most strongly with the start of the net carbon uptake period (r = 0.70, p < 0.001) and the start of the growing season (r = 0.55, p < 0.01). Although the increase of the normalized difference vegetation index also correlates with the start of these periods, a thorough examination of this result shows that the latter happens well before the former. No dependency between any environmental control and the end of the net carbon uptake period was identified. Also, the annual net ecosystem productivity is highly correlated with the length of the net carbon uptake period (r = 0.54, p < 0.01). Other environmental controls such as annual precipitations, the mean annual soil temperature or the maximum yearly normalized difference vegetation index have a smaller impact on the annual net ecosystem productivity. By extending the dataset to include forest stands that represent a wider climate and permafrost variability, we will examine the generalizability of these results.
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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 | 0.001 |
| 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 | 0.000 |
| 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".