Effectiveness of Retention Harvesting for Biodiversity Conservation: Evidence for Understory Vegetation and Wildlife
Bibliographic record
Abstract
Natural disturbance emulation is being used in forest management in an attempt to mitigate the negative effects of harvesting on biodiversity. In the western Canadian boreal forest where the predominant large-scale natural disturbance is wildfire, harvests that leave live mature trees behind at harvest retain some of the structural complexity characteristic of pyrogenic landscapes. Such ‘retention harvests’ are thought to emulate wildfire in a way that conserves biodiversity. Trees can be retained at different levels (percentage of initial basal area retained) and in different patterns (dispersed and aggregated). Although retention harvesting provides for greater structural diversity post-harvest, the forest floor is not burned as it is by wildfire. Therefore, prescribed burning may emulate the influence of wildfires more effectively than does retention harvesting alone. In this dissertation, I explored the effects of different retention levels and patterns, as well as post-harvest prescribed fire, on wildlife and understory vascular plants in the boreal mixedwood forests of northwestern Alberta, Canada. First, I used a combination of midden counts, scat surveys, and camera trapping to compare wildlife use of different stands harvested across a range of dispersed retention levels (0%, 10%, 20%, 50%, 75%, 100%) 15-18 years post-harvest. Second, I examined the effectiveness of combining two retention patch sizes (0.20 ha and 0.46 ha) with different dispersed retention levels (0%, 10%, 20%, 50%, 75%) for supporting understory vascular plant communities that are characteristic of unharvested forest 15 years post-harvest. Third, I investigated the effects of prescribed fire in post-retention harvested stands on understory vascular plant communities up to 12 years post-fire in three different forest cover types (conifer-dominated, mixedwood, deciduous-dominated). Use of harvest stands for late-seral wildlife species was highest in stands having higher retention levels (≥ 50% retention) characterized by high tree basal area and canopy cover. In contrast, lower retention levels (≤ 20% retention) having greater understory cover benefitted early-seral wildlife species. For vascular plants, different retention patch sizes supported distinct understory plant communities with both patch sizes being more effective at supporting late-seral plant communities when surrounded by higher levels of dispersed retention. Prescribed fire benefitted some fire-specialist plant species with the effects of prescribed fire on understory plant communities still evident more than a decade later. Overall, these results suggest that a variety of retention levels, combinations of retention patterns, and the appropriate application of prescribed fire would maintain the structural heterogeneity that supports a wide spectrum of species’ habitats within harvested landscapes. These findings contribute to our understanding, development, and application of effective harvesting practices for sustainable forest management and biodiversity conservation.
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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 | 0.001 |
| 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".