Tree biomass reconstruction shows no lag in postglacial afforestation of eastern Canada
Bibliographic record
Abstract
Forest ecosystems in eastern Canada are particularly sensitive to climate change and may shift from carbon sinks to carbon sources in the coming decades. Understanding how forest biomass responded to past climate change is thus of crucial interest, but past biomass reconstruction still represents a challenge. Here we used transfer functions based on modern pollen assemblages and remotely sensed biomass estimation to reconstruct and quantify, for the last 14 000 years, tree biomass dynamics for the six main tree genera of the boreal and mixedwood forests (Abies, Acer, Betula, Picea, Pinus, Populus). We compared the mean genera and total biomass with climatic (summer temperatures and annual precipitation), physical (CO2, insolation, ice area), and disturbance (burned biomass) variables to identify the potential drivers influencing the long-term trends in tree biomass. For most genera, tree biomass was related to summer temperature, insolation, and CO2 levels; Picea was the exception and its biomass also correlated with annual precipitation. At the onset of the Holocene and during the Holocene Thermal Maximum (ca. 10 000–6000 BP), tree biomass tracked the melting of the Laurentide Ice Sheet with high values (>50 tonnes·ha–1 and a total of 12 Pg). These values, in the range of modern forest ecosystems biomass, indicate that trees were probably able to survive in a periglacial environment and to colonize the region without any discernible lag by tracking the ice retreat. High biomass at the beginning of the Holocene was likely favoured by higher than present insolation, CO2 levels higher than during the Last Glacial Maximum, and temperature and precipitation close to present-day levels. Past tree biomass reconstruction thus brings novel insights about the drivers of postglacial tree biomass and the overall biogeography of the region since the deglaciation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".