A landscape perspective of bird nest predation in a managed boreal black spruce forest
Bibliographic record
Abstract
Several landscape level studies have reported that bird nest predation increases as forest cover decreases. These studies have mainly been conducted in agricultural or urban regions. However, few studies have explored relationships between forest cover and nest predation in boreal forests managed for timber harvesting. In 1997 and 1998, we evaluated bird nest predation in a mosaic of clearcuts and forest remnants dominated by black spruce (Picea mariana [Mill.] B.S.P.) and located north of Lake Saint-Jean, Québec. We used a 7 km × 9 km grid of sampling points to determine nest predation at four landscape scales (local vegetation, and 250 m, 500 m, and 1000 m radii around sampling points). Artificial nests (ground and arboreal) containing a common quail (Coturnix coturnix L.) egg and a plasticine egg were used to calculate predation pressure and to identify nest predators. Nest predation was high over the entire study area. Dominant predators were the gray jay (Perisoreus canadensis L.) and the red squirrel (Tamiasciurus hudsonicus Erxleben). Depredation by squirrels was influenced by local variables in 1997 and by landscape variables in 1998. In the latter case, depredation by squirrels increased as spruce cover increased. Depredation by gray jays was positively related to water body area and jack pine (Pinus banksiana Lamb.) cover. Squirrels preyed more on ground nests than on arboreal nests, while gray jays preyed almost exclusively on arboreal nests. We conclude that these predators probably impose different threats to different songbird species in boreal black spruce forests. Our results show that, in the short term, timber harvesting did not seem to increase predation in a boreal black spruce forest.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".