Modelling the Range Shift of Boreal Tree Species with Limitations by Dispersal Distance and Disturbance-Driven Clearing Availability (Northwestern Ontario)
Bibliographic record
Abstract
Species distribution models (SDMs) of trees often overestimate future range shifts when functional traits and landscape characteristics are not accounted for. Under climate change, dispersal abilities and the availability of forest clearings can be important determinants of range shift success and long-term species persistence. Here, I projected the distributions of 15 tree species in Ontario by imposing spatial limitations based on dispersal distances and proximity to disturbance-driven clearings into Maxent SDMs. Colonizable area for all species was significantly reduced compared to climate envelope models, with long-distance dispersers with large initial distributions best tracking their thermal niche. Projections also showed a greater increase in colonizable area for deciduous species, suggesting a potential shift in forest composition from current conifer dominance. Incorporating dispersal and disturbance history produces SDM projections with improved realism, which will better inform forest conservation and management and shed light on the fate of species under 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".