The structure of boreal old-growth forests changes at multiple spatial scales over decades
Bibliographic record
Abstract
Abstract Context Changes in the structure of boreal old-growth forests are typically studied at a specific spatial scale. Consequently, little is known about forest development across different spatial scales. Objectives We investigated how and at what spatial scales forest structure changed over several decades in three 4 km 2 boreal old-growth forests landscapes in northeastern Finland and two in Quebec, Canada. Methods We used canopy cover values visually interpreted to 0.1-ha grid cells from aerial photographs taken at three time points between the years 1959 and 2011, and error distributions quantified for the interpretation. We identified the spatial scales at which canopy cover changed between the time points, and examined the credibility of changes at these scales using the error distributions in Bayesian inference. Results Canopy cover changed at three to four spatial scales, the number of scales depending on the studied landscape and time interval. At large scales (15.4–321.7 ha), canopy cover increased in Finland during all time intervals. In Quebec, the direction of the large-scale change varied between the studied time intervals, owing to the occurrence of an insect outbreak and a consequent recovery. However, parts of these landscapes also showed canopy cover increase. Superimposed on the large-scale developments, canopy cover changed variably at smaller scales (1.3–2.8-ha and 0.1-ha). Conclusions Our findings support the idea that the structure of boreal old-growth forests changes at discernible spatial scales. Instead of being driven by gap dynamics, the old-growth forests in the studied regions are currently reacting to large-scale drivers by an increase in canopy cover.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".