Retention forestry as a climate solution: Assessing biomass, soil carbon and albedo impacts in a northern temperate coniferous forest
Bibliographic record
Abstract
Forest management pathways for nature-based climate solutions, such as variable retention harvesting (VRH), have been gaining traction in recent years; however, their net biochemical and biophysical impacts remain unknown. Here, we use a combination of close-range and satellite remote sensing, eddy covariance technique, and ground-based biometric measurements to investigate forest thinning density and aggregation that maintain ecosystem nutrients, enhance tree growth and provide a negative feedback to the local climate in a northern temperate coniferous forest stand in Ontario, Canada. Our results showed that soil carbon (C) and nitrogen (N) in VRH plots were significantly lower (p < 0.05) for all VRH treatments compared to unharvested plots. On average, soil C was reduced by −0.64 ± 0.22 Δ% C and N by −0.023 ± 0.008 Δ% N in VRH plots. We also observed the largest loss of soil C and N in open areas of aggregate plots. Furthermore, the changes in albedo resulting from VRH treatment were equivalent to removing a large amount of C from the atmosphere, ranging from 1.3 ± 0.2 kg C yr−1 m−2 in aggregate 33 % crown retention plots to 3.4 ± 0.5 kg C yr−1 m−2 in dispersed 33 % crown retention plots. Our findings indicate that spatially dispersed VRH resulted in minimal loss of soil C and N and the highest understory growth and C uptake, while enhanced tree growth and local cooling through increased albedo were observed in dispersed VRH plots with the fewest residual trees. These findings suggest that using the harvested trees from VRH in a way that avoids releasing C into the atmosphere makes dispersed VRH the preferred forest management pathway for nature-based climate solutions.
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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 | 0.001 |
| 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) | 0.000 | 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".