Simulated impacts of mountain pine beetle and wildfire disturbances on forest vegetation composition and carbon stocks in the Southern Rocky Mountains
Bibliographic record
Abstract
Abstract. Forests play an important role in sequestering carbon and offsetting anthropogenic greenhouse gas emissions, but changing disturbance regimes may compromise the capability of forests to store carbon. In the Southern Rocky Mountains, a recent outbreak of mountain pine beetle (\\textit{Dendroctonus ponderosae}; MPB) has caused remarkable levels of tree mortality. To evaluate the long-term impacts of both this insect outbreak and another characteristic disturbance in these forests, high-severity wildfire, we simulated potential changes in species composition and carbon stocks using the Forest Vegetation Simulator (FVS). Simulations were completed for 3 scenarios (no disturbance, actual MPB infestation, and modeled wildfire) using field data collected in 2010 at 97 plots in the lodgepole-pine-dominated forests of eastern Grand County, Colorado, which were heavily impacted by MPB after 2002. Results of the simulations showed that (1) lodgepole pine remained dominant over time in all scenarios, with basal area recovering to pre-disturbance levels 70–80 yr after disturbance; (2) wildfire caused a greater magnitude of change than did MPB in both patterns of succession and distribution of carbon among biomass pools; (3) levels of standing-live carbon returned to pre-disturbance conditions after 40 vs. 50 yr following MPB vs. wildfire disturbance, respectively, but took 120 vs. 150 yr to converge with conditions in the undisturbed scenario. Lodgepole pine forests appear to be relatively resilient to both of the disturbances we modeled, although changes in climate, future disturbance regimes, and other factors may significantly affect future rates of regeneration and ecosystem response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".