Potential redistribution of tree species habitat under five climate change scenarios in the eastern US. Forest Ecology and Management 155:205–222
Bibliographic record
Abstract
Global climate change could have profound effects on the Earth’s biota, including large redistributions of tree species and forest types. We used DISTRIB, a deterministic regression tree analysis model, to examine environmental drivers related to current forest-species distributions and then model potential suitable habitat under five climate change scenarios associated with a doubling of atmospheric CO2. Potential shifts in suitable habitat for 76 common tree species in the eastern US were evaluated based on more than 100,000 plots and 33 environmental variables related to climate, soils, land use, and elevation. Regression tree analysis was used to devise prediction rules from current species–environment relationships. These rules were used to replicate the current distribution and predict the potential suitable habitat for more than 2100 counties east of the 100th meridian. The calculation of an importance value-weighted area score, averaged across the five climate scenarios, allowed comparison among species for their overall potential to be affected by climate change. When this score was averaged across all five climate scenarios, 34 tree species were projected to expand by at least 10%, while 31 species could decrease by at least 10%. Several species (Populus tremuloides, P. grandidentata, Acer saccharum, Betula papyrifera, Thuja occidentalis) could have their suitable habitat extirpated from US. Depending on the scenario, the optimum latitude of suitable habitat moved north more than 20 km for 38–47 species, including 8–27 species more than 200 km or into Canada. Although the five scenarios were in general agreement with respect to the overall tendencies in potential future suitable habitat, significant
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".