Examining climate-biome (“cliome”) shifts for Yukon and its protected areas
Bibliographic record
Abstract
Protected area networks are the foundation of conservation, even in northern Canada where anthropogenic impact on the landscape is currently limited. However, the value of protected areas may be undermined by climate change in this region where the rate and magnitude is high, and shifts in vegetation communities and associated wildlife species are already underway. Key to developing responses to these changing conditions is anticipating potential impacts and the risks they pose. Capitalizing on an existing modeled dataset for Yukon from Scenarios Network for Alaska and Arctic Planning (SNAP), we examine projected shifts in the distribution of 18 clusters of climate parameters, and the vegetation communities currently associated with them (collectively termed “cliomes”) across three 30-year time steps, from the present through the 2090s. By the 2090s, Yukon may lose seven cliomes and gain one. Three regional changes, if accompanied by vegetation redistribution, represent biome shifts: complete loss of climate conditions for arctic tundra in northern Yukon; emergence of climate conditions supporting grasslands in southern Yukon valleys; reduction in climates supporting alpine tundra in favor of boreal forests types across the mountains of central and northern Yukon. Projections suggest that, by the end of the 21st century, higher elevations in southern Yukon change least when compared to the turnover in cliomes exhibited by the high latitude, arctic parks to the north. This analysis can assist with: planning connectivity between protected areas; identifying novel conservation zones to maximize representation of habitats during the emerging changes; designing plans, management and monitoring for individual protected areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".