A Comparison of Deadwood Characteristics in Post-harvest and Post-fire Island Remnants in Upper Foothill Natural Subregion of Alberta
Bibliographic record
Abstract
Across the globe, forest management has evolved from a limited focus on timber extraction to management for a much broader set of values. Through this evolution, Ecosystem-Based Management (EBM) has emerged as a pivotal strategy, with Natural-Disturbance-Based Management (NDBM) aiming to minimize anthropogenic impacts on forest ecosystems. Although retention practices in boreal forests are informed by natural disturbance, i.e. patterns of forest structure remaining after wildfires, few studies have compared the structure and diversity of patch retention and fire skips (islands). Understanding this comparison is crucial for assessing whether retention patches can serve as effective ecological analogs to fire skips, particularly as their structural characteristics may diverge or converge over time. Coarse woody debris (CWD) is an important structural element influenced by forest management worldwide. In this study, we compared the abundance and spatial distribution of recent deadwood across post-fire and post-harvest remnant islands in Upper foothill Natural Subregion of Alberta, Canada, approximately a decade after disturbance. We surveyed 28 sites (14 fire-origin, 14 harvest-origin), each comprising seven circular plots (7.3 m radius) along a 90 m transect, and measured CWD attributes using the line-intersect method. I tested for differences in recent deadwood volume, abundance and mean diameter of snag, log and live trees between post fire and post-harvest retention patches along with their reference forests (closest natural undisturbed forests) using mixed effects models. A power analysis confirmed that the model had sufficient sensitivity (power >80%) to detect differences between disturbance types. The results showed no significant differences in total recent CWD volume between fire and harvest islands, suggesting that structurally designed harvest patches can retain deadwood volumes comparable to those following natural disturbance. Furthermore, deadwood volumes in remnant interiors were statistically similar to those in undisturbed reference forests, highlighting the potential of islands regardless of origin as structural analogues in managed landscapes. Edge-to-interior gradients showed that fire islands retained fewer but larger structural elements, while harvest islands exhibited higher stem abundance of smaller size. Initial stand volume (ISV) emerged as the strongest predictor of CWD accumulation, with a pronounced increase beyond 200 m³/ha. These results underscore the importance of selecting high-ISV patches for retention and suggest that ecologically-informed harvest designs can effectively preserve structural complexity and legacy functions in boreal forest systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".