Vegetation changes in Jasper National Park assessed from resampling of ecological land classification plots established in the 1970s
Bibliographic record
Abstract
Canada’s mountain ecosystems are changing as a result of climate change and a host of natural and anthropogenic disturbances. Understanding the kinds, rates, and causes for those changes is important for informed ecosystem management. To assess changes in the vegetation of Jasper National Park (JNP), we documented changes in plant community composition by resampling 41 ecological land classification (ELC) plots first assessed in the 1970s. In 2023, we documented the presence and percent cover of vascular, hepatic, moss, and lichen species within each plot and compared those data to the 1970s data. Within each plot, we determined the change in cover for each taxon. The communities have become more species rich since the 1970s and community type diversity has increased. Despite rates of species turnover that exceed 50%, plant diversity shows no signs of decline. Multidecadal succession, perhaps influenced by climatic change and human disturbances, is altering the vegetation composition independent of wildfire and mountain pine beetle. Ecologically important species that decreased over time included Pinus contorta, Rosa acicularis, Vaccinium cespitosum, V. scoparium, and V. vitis-idaea. Important increasers included Picea glauca, Pseudotsuga menziesii, and Hylocomium splendens. Younger and drier sites changed more than did older and moister sites.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".