Paludification reduces black spruce growth rate but does not alter ecophysiological mechanisms in Canadian boreal forested peatlands
Bibliographic record
Abstract
Abstract Background Paludification is widespread in the boreal biome, inducing tree growth decline in forested peatlands following the development of thick organic layers over the mineral soil. However, the ecophysiological processes involved remain poorly documented and little is known about the interactions between tree growth mechanisms and site conditions in these ecosystems. We investigated changes in stem growth and main ecophysiological processes in a black spruce forested peatland in eastern Canada by combining peat-based and tree-ring stable isotope analyses. These were conducted at three sampling sites located along a paludification gradient with different peat thicknesses.Results Organic layer thickening induces black spruce growth decline without altering tree ecophysiological mechanisms. A 40% increase in water use efficiency, or the ratio of carbon assimilated to water losses, was observed at the three sites from 1920 to the 1980s, but did not translate into enhanced tree growth. A clear shift in the 1980s revealed a decline in black spruce sensitivity to climate and rising atmospheric CO2 concentration, regardless of the organic layer thickness. Water table reconstructions revealed an important drawdown in the last few decades at the three sites, but we found no evidence of an influence of water table variations on stem growth.Conclusions This study shows that paludification induces black spruce growth decline without altering tree metabolism in boreal forested peatlands. This underlines that changes in water use efficiency are decoupled from changes in carbon allocation, which are constrained by site, or even tree-specific strategies to access water and nutrients from belowground. Our findings indicate that dynamic changes in edaphic conditions need to be considered in process models. Otherwise, failing to account for the degree of paludification can lead to misleading forest productivity predictions and result in considerable overestimations of aboveground carbon stocks from trees in the boreal regions.
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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 | 0.001 | 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".