Forest closure and encroachment at the grassland interface: a century‐scale analysis using oblique repeat photography
Bibliographic record
Abstract
Abstract We used repeat oblique photography to quantify and determine the drivers of vegetation change, particularly forest closure and encroachment, in the Rocky Mountains of southern Alberta, Canada, from the beginning of the twentieth century to the present. We classified the landscape into seven distinct vegetation types (closed‐canopy conifer forest, broadleaf deciduous forest, mixedwood forest, open‐canopy woodlands, shrublands, grasslands and meadows, non‐vegetated) and assessed vegetation change between the two time periods. We found that closed‐canopy coniferous forest, broadleaf deciduous forest, and mixedwood forest increased on an area basis by 35%, 45%, and 80%, respectively, over this time period; concomitantly, grasslands and open‐canopy woodlands declined by 25% and 39%, respectively. Overall, 28% of the landscape was in a more advanced successional state in 2008 as compared to the early twentieth century. The Montane and Subalpine Natural Subregions (NSR) experienced the most change (42% and 26%, respectively, in a more advanced successional state). The loss of open‐canopy woodlands was observed across the entire landscape, while grassland and meadow losses were most acute in the Subalpine and Alpine NSRs. The probability of vegetation change to a more advanced successional condition was greater at higher elevations and in areas receiving lower amounts of solar insolation. The changes observed are consistent with what we would expect to see due to lengthening of fire return intervals. Understanding the magnitude of change in vegetation types and the drivers of this change is important for the development of effective contemporary ecosystem management and restoration practices.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".