Predicting carbon storage in North American maritime boreal forests under combined disturbances
Bibliographic record
Abstract
Understanding how forest disturbances, such as fire and herbivory, affect carbon storage across the landscape can help inform forest management and disturbance mitigation. However, this is made difficult by uncertainties in carbon predictions and limited records of disturbance histories. Our objectives were to predict carbon stocks and predict where disturbances have created open forest patches across the study areas, and to assess the relative effects of disturbances and subsequent moose herbivory on carbon stocks. We used field measurements of carbon stocks and disturbances from a two-year field study on the island of Newfoundland, Canada, along with remotely sensed environmental variables (e.g., stand height, elevation), to predict spatial patterns in carbon stocks as well as predict where disturbances have created open forest across Gros Morne and Terra Nova National Parks. We found that the remotely sensed variables of forest height and productivity were the most informative predictor variables for both carbon stocks and whether an area was an open forest. Our models predict less carbon in areas classified as open forest relative to areas classified as mature forest. Further, we observed that moose herbivory may impede the recovery of carbon stocks in open forests after disturbances, leading to a reduction in carbon storage of up to 13 megatonnes, or 40% of carbon predicted to be stored across the two national parks. Overall, we find there is potential to increase or maintain carbon storage in maritime boreal forests by limiting moose herbivory in areas regenerating following disturbance. This work adds to our understanding of drivers of forest carbon storage and can help inform boreal forest management to optimize carbon storage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".