The effects of moose browsing on balsam fir and forest recovery vary with bioclimatic and human use across the island of Newfoundland
Bibliographic record
Abstract
Moose present a complex management problem because they generate a mixture of benefits and costs to humans, some of which are caused by browsing of regenerating trees. We developed a Lotka–Volterra model, parameterized by moose management areas, to link moose browsing to spruce and balsam fir dynamics on the island of Newfoundland. The model predicts the distribution of moose, adult fir, and spruce well. Empirical estimates of juvenile fir biomass were variable, and our model predicted its biomass poorly. Our model predicts a small negative effect of moose on adult fir biomass (−0.06%) and juvenile fir biomass (−1.65%) and a small positive effect on spruce biomass (+0.02%) under baseline assumptions, but larger effects (±10%–60%) if moose browse commercial softwoods preferentially. Small effects of moose on trees at steady state do not fully reflect the importance of moose because moose parameters (e.g., growth rate and harvesting rate) impacted the return time of our model from disturbance. Our model analysis demonstrates one way to add animal effects into vegetation growth models and suggests that parameterizing ecological models by management unit is useful when the data to support more detailed models are not available.
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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 | 0.001 |
| 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) | 0.001 | 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".