Response of corvid nest predators to thinning: implications for balancing short- and long-term goals for restoration of forest habitat
Bibliographic record
Abstract
Forest thinning on public lands in the Pacific Northwest USA is an important tool for restoring diversity in forest stands with a legacy of simplified structure from decades of intensive management for timber production. A primary application of thinning in young (< 50-year-old) stands is to accelerate forest development to mitigate loss of late-seral habitat to decades of logging. However, thinning may have short-term negative effects for some species associated with mature forest that are expected to benefit from the practice over the long term. An increased risk of nest predation is a primary concern to managers charged with stewardship of habitat for the federally threatened Marbled Murrelet (Brachyramphus marmoratus), a species that nests in older forests. Predation by corvids is the greatest cause of nest failure for the Marbled Murrelet, and corvids are known to respond positively to forest disturbance, but quantitative information is lacking on the potential impacts of thinning on risk of nest predation. We investigated the response of two common corvid nest predators, Steller’s Jay (Cyanocitta stelleri) and Canada Jay (Perisoreus canadensis), to variation in thinning intensity in young forest (< 50 years old) using data from a long-term silviculture experiment. We used a Before-After-Control-Impact (BACI) design, linear mixed modeling, and occupancy modeling to quantify differences in corvid observation rates among varying levels of thinning intensity, and to assess changes in jay response over more than a decade following thinning. We found an increase in observation rates of both species in the heavily thinned treatment during the first 5 to 7 years following thinning, and some evidence of a short-term increase in Steller’s Jay activity in the thinning-with-gaps treatment. Neither jay species responded to the least intensive thinning treatment, which reduced average canopy cover by < 30%. By approximately a decade after thinning, observation rates of jays did not differ between unthinned controls and any of the thinning treatments. Incorporating our quantitative information into landscape-level planning can help managers balance short- and long-term conservation goals.
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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 | 0.000 | 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) | 0.001 | 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".