Climate adaptation scenarios for a resilient future at University of British Columbia Botanical Garden: Modeling 4 Species of Acer (Maple)
Bibliographic record
Abstract
As anthropogenic climate change continues to disrupt forests and species’ ecological niches, there is increasing urgency to create plans surrounding adaptation and mitigation for especially vulnerable species. The University of British Columbia’s Botanical Garden (UBCBG) wants to understand species responses to climate change and whether species within their collection will be able to survive in the Garden, given the effects of climate change. One especially important collection UBCBG curates is maple (Acer) trees, currently leading the global consortium of Acer and housing over 50 different species. This study examined the survival probability of four Acer species UBCBG curates including: five-fingered Maple (Acer pentaphyllum Diels), considered critically endangered; paperbark/bloodbark Maple (Acer griseum (Franch.) Pax), considered endangered; bigleaf maple (Acer macrophyllum Pursh), not endangered; and vine maple (Acer circinatum Pursh), not endangered. Maxent, a popular machine-learning algorithm, was used with open-source WorldClim’s 19 bioclimatic variables and provided presence-only occurrence data to conduct species distribution models for each tree species. Each model was evaluated using area under the curve (AUC). AUC scores were considered ‘good’ for each model; A. griseum - 0.95; A. pentaphyllum - 0.877; A. macrophyllum - 0.986; A. circinatum - 0.976. However, future distribution maps contain questionable results due to insufficient field data to inform the model, such as in-field temperature, moisture, elevation, and surrounding vegetation data. Though results can be interpreted as binary regarding whether or not UBCBG will be suitable for each species, they should be taken as preliminary ideas where management plans for mitigation and adaptation can be developed. This study concludes by detailing the importance of field data collection and provides future directions for research that UBCBG may consider when conducting similar analysis.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".