Landscape-Scale Forest Reorganization Following Insect Invasion and Harvest Under Future Climate Change Scenarios
Bibliographic record
Abstract
Abstract Emerald ash borer (EAB; Agrilus planipennis Farimaire) has been found in 35 US states and five Canadian provinces. This invasive beetle is causing widespread mortality to ash trees ( Fraxinus spp.), which are an important timber product and ornamental tree, as well as a cultural resource for some Tribes. The damage will likely continue despite efforts to impede its spread. Further, widespread and rapid ash mortality as a result of EAB is expected to alter forest composition and structure, especially when coupled with the regional effects of climate change in post-ash forests. Thus, we forecasted the long-term effects of EAB-induced ash mortality and preemptive ash harvest (a forest management mitigation strategy) on forested land across a 2-million-hectare region in northern Wisconsin. We used a spatially explicit and spatially interactive forest simulation model, LANDIS-II, to estimate future species dominance and biodiversity assuming continued widespread ash mortality. We ran forest disturbance and succession simulations under historic climate conditions and three downscaled CMIP5 climate change projections representing the upper bound of expected changes in precipitation and temperature. Our results suggest that although ash loss from EAB or harvest resulted in altered biodiversity patterns in some stands, climate change will be the major driver of changes in biodiversity by the end of century, causing increases in the dominance of southern species and homogenization of species composition across the landscape.
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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 | 0.000 |
| 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) | 0.001 | 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 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".