Tree ring evidence for limited direct CO<sub>2</sub> fertilization of forests over the 20th century
Bibliographic record
Abstract
The effect that rising atmospheric CO2 levels will have on forest productivity and water use efficiency remains uncertain, yet it has critical implications for future rates of carbon sequestration and forest distributions. Efforts to understand the effect that rising CO2 will have on forests are largely based on growth chamber studies of seedlings, and the relatively small number of FACE sites. Inferences from these studies are limited by their generally short durations, artificial growing conditions, unnatural step‐increases in CO2 concentrations, and poor replication. Here we analyze the global record of annual radial tree growth, derived from the International Tree ring Data Bank (ITRDB), for evidence of increasing growth rates that cannot be explained by climatic change alone, and for evidence of decreasing sensitivity to drought. We find that approximately 20 percent of sites globally exhibit increasing trends in growth that cannot be attributed to climatic causes, nitrogen deposition, elevation, or latitude, which we attribute to a direct CO2 fertilization effect. No differences were found between species in their likelihood to exhibit growth increases attributable to CO2 fertilization, although Douglas‐fir (Pseudotsuga menziesii) and ponderosa pine (Pinus ponderosa), the two most commonly sampled species in the ITRDB, exhibit a CO2 fertilization signal at frequencies very near their upper and lower confidence limits respectively. Overall these results suggest that CO2 fertilization of forests will not counteract emissions or slow warming in any substantial fashion, but do suggest that future forest dynamics may differ from those seen today depending on site conditions and individual species' responses to elevated CO2.
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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 | 0.001 | 0.002 |
| 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) | 0.003 | 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".