Modelling Effects of Partial Harvesting on Wildlife Species and their Habitat
Bibliographic record
Abstract
In Canada’s eastern boreal forest region, partial-harvest silviculture has garnered increasing support for maintaining wildlife species and habitat structure associated with late-successional forests. If late-successional species can find suitable habitat in managed stands that retain a certain number, type, and pattern of live trees, then partial harvesting might represent a viable tool for maintaining species associated with old and complex forests. I used several indirect forms of inference to evaluate whether late-successional vertebrate species can be maintained within partially harvested stands in the eastern boreal forest. A meta-analysis of studies across North America showed that no bird species decreased in abundance by half where light harvesting retained at least 70% of live trees. However, adverse effects occurred at lower levels of retention, with some bird species unlikely to use harvested stands with less than 50% retention until appropriate habitat structure returned. A spatially explicit stand dynamics model showed that while partial harvesting can promote development of understory saplings, downed wood, and heterogeneity, it can also induce long-term decreases in the abundances of large trees and snags. Consequently, species dependent on the latter, such as brown creepers (Certhia americana) and northern flying squirrels (Glaucomys sabrinus), were projected to be more susceptible to partial harvesting than those associated with other types of structure. At a more detailed scale, a neighbourhood model developed from live-trapping data revealed that southern red-backed voles (Myodes gapperi) exhibited local associations with several late-successional features within boreal mixedwood stands. Their associations with some features depended on stand-level habitat conditions, which suggested that vole habitat in managed stands could be improved by retaining live trees and downed wood. A spatially explicit model of optimal home range establishment that incorporated these relationships fit vole abundance data marginally better than an aspatial habitat model. When the home range model was applied to simulated partially harvested stands, it predicted that spatial heterogeneity could have a positive effect on vole abundance, but only at harvest intensities of 70-90% with suppressed shrub cover. With careful attention to issues such as these, partial-harvest silviculture could be useful in maintaining vertebrate biodiversity within eastern boreal forests.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".