Do harvest retention patches in the boreal forest emulate those resulting from wildfire? A comparison of understory vegetation a decade after disturbance
Bibliographic record
Abstract
Abstract To sustain a breadth of ecosystem services, ecosystem‐based forest management aims to reduce differences between managed and natural forests. Based on the observed structural complexity of forests following natural disturbance, retention of forest structure at harvest is being implemented globally. Despite a decade of including retention patches in managed forests, it remains unclear if patches in forests disturbed by fire and harvest exhibit similar structural characteristics and biodiversity as intact forest. Such knowledge is critical within an adaptive management framework. We present the first study comparing understory vegetation of post‐fire and post‐harvest remnants in the boreal. A decade following disturbance, we examine forest structure and plant diversity in key locations of harvests and burns: the disturbed matrix, island remnants within the disturbance, adjacent undisturbed forest and edges of these. We utilise a trait‐based framework for plant diversity to test hypotheses about how traits vary in relation to disturbance type, environmental variables and with variation in structure. Both harvest and wildfire remnants maintained understory communities and forest structure similar to adjacent forest, but remnant and reference areas exhibited some compositional differences between fire and harvest sites. Edge effects were minor and comparable between burns and harvests. Analysis of plant functional traits revealed similar patterns across burns and harvests, with colonisation traits associated with disturbed areas, while persistence traits were associated with island remnants. Synthesis and applications. The similarity of the understory community and forest structure of island remnants to adjacent reference forest in burned and harvested areas suggests remnants are effective reservoirs of undisturbed forest. This provides evidence that implementation of aggregated retention at harvest helps achieve a key objective of retention, that is, maintaining structural heterogeneity to support biodiversity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".