Climate change and modelled biome representation in Canada's national park system: implications for system planning and park mandates
Bibliographic record
Abstract
ABSTRACT Aim The study examined the potential for change in biome representation within Canada's national park system under multiple climate change scenarios and subsequent potential vulnerabilities in Parks Canada policy and planning frameworks. Location The study was conducted for Canada's 39 national parks. Methods The vegetation change scenarios were based on modelling results from the BIOME3 and MAPSS equilibrium process‐based global vegetation models (GVM), run with multiple doubled‐CO2 climate change scenarios. The six vegetation distribution scenarios were calculated at 0.5° latitude–longitude resolution and the boundaries of 39 national parks superimposed in a geographic information system (GIS). Park management plans and other planning documents were also reviewed as part of the analysis. Results The proportional distribution of biomes in Canada's national park system was very similar (within 3% of area for each biome) using BIOME3 and MAPSS under the current climate. Regardless of the GVM and climate change scenario used, the modelling results suggest the potential for substantial change in the biome representation in Canada's national park system. In five of six vegetation scenarios, a novel biome type appeared in more than half of the national parks and greater than 50% of all vegetation grid boxes changed biome type. The proportional representation of tundra and taiga/tundra in the national park system declined in each of the vegetation scenarios, while more southerly biomes (temperate forests and savanna/woodland) increased (in some scenarios doubling to quadrupling). Results for boreal forest varied among the climate change scenarios. A range of potential vulnerabilities in existing policy and planning frameworks were identified, including the national park system plan, individual park objectives, and fire and exotic species management plans. Conclusions Climate change represents an unprecedented challenge to Parks Canada and its ability to achieve its conservation mandate as presently legislated. Research is needed not only on ecosystem responses to climate change, but also on the capacity of conservation systems and agencies to adapt to climate change.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".