Drivers of elemental storage and cycling in boreal forests: evaluating the effects of forest disturbances and an introduced ungulate
Bibliographic record
Abstract
Selective browsing by ungulates alters forest structure and composition with the potential to suppress forest regeneration. Research suggests that ungulate impacts may be stronger in recently disturbed forests and in novel environments (i.e., introduced ungulates). In this thesis, we used observational and experimental (i.e., paired exclosure-controls) data to test the hypothesis that non-native moose and forest disturbances (i.e., fires and insect outbreaks) have negative impacts on carbon storage (i.e., total, aboveground, and belowground carbon) and plantavailable nitrogen in Newfoundland’s boreal forests. Using our observational data, we found that forest disturbances were a key driver of carbon storage dynamics, but we did not find a relationship between moose densities and carbon storage. We also found that supply rate of ammonium was negatively correlated with soil temperature and positively correlated with moose density. Using our experimental data, we did not detect any effect of disturbance history or moose presence on carbon storage or ammonium supply rates after 24-27 years of moose exclusion. This work demonstrates the impacts of natural disturbances and herbivory on forest ecosystem functions, such as carbon sequestration. Our findings will help natural resource managers consider the effects of moose and disturbances when developing nature-based solutions to climate change.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".