Effects of Postfire Snag Removal on Breeding Birds of Western Labrador
Bibliographic record
Abstract
The issue of snags in wildlife management has progressed from speculation about their importance (Elton 1966), to statements of wildlife dependence on them (Thomas et al. 1979), to objectives for maintaining snags to conserve biodiversity during timber extraction (e.g., Woodley and Forbes 1997, Lehmkuhl et al. 2003). Snag management usually involves the maintenance of a small proportion of snags or dying trees (e.g., 10 trees or snags .50-cm dbh per ha). More problematic is the management of entire stands of dead trees (snag-forests) resulting from fire or insect epizootics. Demands for wood products require managers to rapidly harvest snag-forests to reclaim lost timber, but large-scale salvage (e.g., 28,000 km in Oreg. and Wash. in the 1980s [Youngblood and Wickman 2002]) likely effects snag-dependent wildlife (DellaSalla et al. 1995, McIver and Starr 2001, Nappi et al. 2004). Fire suppression and insect spray programs have further reduced the extent of snagforests (Trani et al. 2001) while the focus on maintaining old-growth forest has deflected attention from snag-forest conservation (Askins 2001, Hunter et al. 2001). From a wildlife perspective, snag-forests are a combination of early successional vegetation and large dead trees that are short-lived as snags. These ephemeral forests are critical for birds dependent on Coleoptera larvae (Raphael et al. 1987, Hutto 1995, Nappi et al. 2003). Despite the ecological importance of snag-forests, salvage-logging effects are poorly understood (McIver and Starr 2001). The few existing studies are correlative, focusing on snag-nesting species (Saab and Dudley 1998, McIver and Starr 2001). Salvage logging should affect the entire bird community (Hutto 1995, LeCoure et al. 2000, Morrisette et al. 2002) because snags are the dominant structure on early burns. Several studies associate noncavity nesters with snagforests (Hutto 1995, Schwab et al. 2001, Simon et al. 2002), but it is unclear whether they are attracted to the early successional vegetation or to the snags. This makes it impossible to draw firm conclusions about the effects of salvage logging. The few studies on salvage logging (e.g., LeCoure et al. 2000, Morrisette et al. 2002) did not examine different intensities of logging required to provide managers with options. As a result, we conducted a before and after experiment with 3 intensities of snag removal in western Labrador, Canada. Our objective is to provide forest managers with opening-size thresholds for specific bird species to help guide salvage logging in snag-forests.
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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 | 0.001 | 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".