Response of carbon and water fluxes to environmental variability in two Eastern North American forests of similar-age but contrasting leaf-retention and shape strategies
Bibliographic record
Abstract
Abstract. The annual carbon and water dynamics of two Eastern North American forests were compared over a six year period from 2012 to 2017. The geographic location, forest age, soil, and climate were similar between the sites, however, the species composition varied: one was a deciduous broadleaf forest, while the other an evergreen needleleaf forest. During the 6-year study period, the mean annual net ecosystem productivity (NEP) of the coniferous forest was slightly higher and more variable (218 ± 109 g C m−2 yr−1) compared to that of the deciduous broadleaf forest NEP of 200 ± 83 g C m−2 yr−1. Similarly, the mean annual evapotranspiration (ET) of the conifer forest over the 6-year study period was higher (442 ± 33 mm yr−1) compared to that of the broadleaf forest (388 ± 34 mm yr−1), but with similar interannual variability. Significant abnormalities in fluxes were measured between sites during drought years. Summer meteorology greatly impacted fluxes at both sites, but to varying degrees and with varying responses. In general, warm temperatures caused higher ecosystem respiration (RE), resulting in reduced mean annual NEP values – an impact that was more pronounced at the deciduous broadleaf forest compared to the evergreen needle-leaf forest. However, during drought years, the evergreen forest saw greater annual reduction in carbon sequestration compared to the deciduous forest. In the evergreen conifer forest, variability of summer meteorology greatly controlled the forest's annual carbon sink-source strength. Annual ET at both forests was driven by changes in air temperature (Ta), with the largest annual ET measured in the warmest years in the deciduous forest. Additionally, prolonged dry periods with increased Ta, greatly reduced ET. During drought years, the carbon and water fluxes of the deciduous forest were less sensitive to changes in temperature or water availability compared to the evergreen forest. If longer periods of increased temperatures and larger precipitation variability during summer months are to be expected under future climates, our findings suggest the carbon sink capacity of the deciduous forest will continue, while that of the conifer forest remains uncertain in the study region.
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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 | 0.001 |
| 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) | 0.001 | 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".