Chronic water stress reduces tree growth and the carbon sink of deciduous hardwood forests
Bibliographic record
Abstract
Predicted decreases in water availability across the temperate forest biome have the potential to offset gains in carbon (C) uptake from phenology trends, rising atmospheric CO2 , and nitrogen deposition. While it is well established that severe droughts reduce the C sink of forests by inducing tree mortality, the impacts of mild but chronic water stress on forest phenology and physiology are largely unknown. We quantified the C consequences of chronic water stress using a 13-year record of tree growth (n = 200 trees), soil moisture, and ecosystem C balance at the Morgan-Monroe State Forest (MMSF) in Indiana, and a regional 11-year record of tree growth (n > 300 000 trees) and water availability for the 20 most dominant deciduous broadleaf tree species across the eastern and midwestern USA. We show that despite ~26 more days of C assimilation by trees at the MMSF, increasing water stress decreased the number of days of wood production by ~42 days over the same period, reducing the annual accrual of C in woody biomass by 41%. Across the deciduous forest region, water stress induced similar declines in tree growth, particularly for water-demanding 'mesophytic' tree species. Given the current replacement of water-stress adapted 'xerophytic' tree species by mesophytic tree species, we estimate that chronic water stress has the potential to decrease the C sink of deciduous forests by up to 17% (0.04 Pg C yr(-1) ) in the coming decades. This reduction in the C sink due to mesophication and chronic water stress is equivalent to an additional 1-3 days of global C emissions from fossil fuel burning each year. Collectively, our results indicate that regional declines in water availability may offset the growth-enhancing effects of other global changes and reduce the extent to which forests ameliorate climate warming.
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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) | 0.001 | 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".