A Climate Suitability Analysis for Western Hemlock: Modeling Future Occurrence Probability of Tsuga heterophylla at the UBC Vancouver Campus
Bibliographic record
Abstract
To manage future green spaces on campus and adopt to the changing climate, the Social Ecological Economic Development Studies (SEEDS) program of the University of British Columbia (UBC) organized a series of projects to predict the climate suitability (occurrence probability) of some common tree species existing at the UBC Vancouver campus by 2100. With a generally low climate suitability, coniferous trees are more vulnerable to the change of precipitation and temperature, and climate change is threatening the growth of coniferous trees in British Columbia, Canada. Western hemlock (Tsuga heterophylla) is a common coniferous tree species occurring at UBC Vancouver campus and was chosen to be analyzed in this study. The analysis of climate suitability was done using a MaxEnt model. As a machine-learning algorithm not requiring data about absent points, MaxEnt is considered to be a good tool for predicting the distribution of species. There were 19 bioclimatic variables tested in the analysis, and the precipitation in the coldest quarter of the year was determined to be the most important climatic variable affecting the growth of Western hemlock. The results showed that the occurrence probability of Western hemlock would drop from its current ~87% to ~10% under one of the most likely climate models in 2100. Although this result could not represent the real occurrence probability of Western hemlock by the end of the century due to the limited data and variables considered (climate only), this study provided a reference for future climate suitability analysis at UBC Vancouver campus of other tree species and could potentially help with the protection of existing Western hemlock at the campus.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".