Scaling ozone responses of forest trees to the ecosystem level in a changing climate
Bibliographic record
Abstract
ABSTRACT Many uncertainties remain regarding how climate change will alter the structure and function of forest ecosystems. At the Aspen FACE experiment in northern Wisconsin, we are attempting to understand how an aspen/birch/maple forest ecosystem responds to long‐term exposure to elevated carbon dioxide (CO2) and ozone (O3), alone and in combination, from establishment onward. We examine how O3affects the flow of carbon through the ecosystem from the leaf level through to the roots and into the soil micro‐organisms in present and future atmospheric CO2conditions. We provide evidence of adverse effects of O3, with or without co‐occurring elevated CO2, that cascade through the entire ecosystem impacting complex trophic interactions and food webs on all three species in the study: trembling aspen (Populus tremuloidesMichx.), paper birch (Betula papyriferaMarsh), and sugar maple (Acer saccharumMarsh). Interestingly, the negative effect of O3on the growth of sugar maple did not become evident until 3 years into the study. The negative effect of O3effect was most noticeable on paper birch trees growing under elevated CO2. Our results demonstrate the importance of long‐term studies to detect subtle effects of atmospheric change and of the need for studies of interacting stresses whose responses could not be predicted by studies of single factors. In biologically complex forest ecosystems, effects at one scale can be very different from those at another scale. For scaling purposes, then, linking process with canopy level models is essential if O3impacts are to be accurately predicted. Finally, we describe how outputs from our long‐term multispecies Aspen FACE experiment are being used to develop simple, coupled models to estimate productivity gain/loss from changing O3.
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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".