Interactions of multiple disturbances in shaping boreal forest dynamics: a spatially explicit analysis using multi‐temporal lidar data and high‐resolution imagery
Bibliographic record
Abstract
Summary 1. Mixed‐wood boreal forests are often considered to undergo directional succession from shade‐intolerant to shade‐tolerant species. It is thus expected that overstorey gaps should lead to the recruitment of shade‐tolerant conifers into the canopy in all stand development stages and that the recruitment of shade‐intolerant hardwoods would be minimal except in the largest gaps. 2. We analysed short‐term gap dynamics over a large 6‐km 2 spatial area of mixed‐wood boreal forest across a gradient of stands in different developmental stages with different times of origin since fire (expressed as stand ‘age’) that were affected differentially by the last spruce budworm (SBW) outbreak. Structural measurements of the canopy from lidar data were combined with spectral classification of broad species groups to characterize the gap disturbance regime and to evaluate the effect of gap openings on forest dynamics. 3. Estimated annual gap opening rates increased from 0.16% for 84‐year‐old stands to 0.88% for 248‐year‐old stands. Trees on gap peripheries in all stands were more vulnerable to mortality than interior canopy trees. 4. Due to recovery from the last SBW outbreak 16 years previously, gap closure rates were higher than opening rates, ranging from 0.44% to 2.05% annually, but did not show any relationship with stand age. There was, however, a continuing legacy of the last SBW outbreak in old‐conifer stands in terms of a continued high mortality of conifers. In all stands, the majority of the openings were filled from below, although a smaller but significant proportion filled from lateral growth of gap edge trees. 5. Synthesis . The forest response to moderate‐ to small‐scale disturbances in old‐growth boreal forest counters the earlier assumption that the transition from one forest state to the next is slow and directional with time since the last fire. Overall, a small 6% increase in hardwoods was observed over 5 years, largely due to regeneration in‐filling of hardwoods in gaps instead of successional transition to more shade‐tolerant conifers. Gaps are vital for hardwood maintenance while transition to softwoods can occur without perceived gap‐formation as overstorey trees die, releasing understorey trees.
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 | 0.000 | 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) | 0.000 | 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".