Nest predation on forest songbirds in a western boreal forest landscape altered by energy sector linear features
Bibliographic record
Abstract
Nest predation is a major source of reproductive failure for many species of songbirds. Habitat fragmentation by human land use creates edge habitat that can alter predator-prey dynamics, create ecological traps, and reduce the amount of high quality habitat available for sustaining bird populations. I studied the nesting success of boreal forest songbirds in two regions of western Canada fragmented by pipelines, seismic lines, and service roads. These linear features result in relatively little forest loss but create vast amounts of edge. Our ability to predict the effect of these edges is hampered by incomplete or inaccurate knowledge about what predators depredate nests and how those predators respond to edges. My objective was to determine if edges were negatively impacting songbird nest success through increased rates of nest predation and whether birds were preferentially using habitats with higher reproductive potential. Using video monitoring, I identified 11 species of nest predators at 71 songbird nests. Red squirrels were the dominant nest predator in both regions and all predators were endemic boreal species rather than non-forest species. I did not find strong evidence that the spatial distribution or probability of nest predation by the majority of nest predators was strongly affected by edge proximity. Of all the predators monitored, only bears and deer mice were more common near edges but they depredated few nests. I also did not find strong support for a negative edge effect of linear features on songbird nest fate (n = 571 nests) relative to forest interiors. Ground nest survival was marginally higher near edges and ground and shrub nest survival was marginally higher where squirrels were absent. In contrast, the survival of canopy nests was higher away from the edge and in the presence of squirrels. Abundance of singing males and nest fate of each guild responded similarly to edges and squirrels indicating birds are preferentially using habitats with higher reproductive potential. Uncertainties in field-based estimates of nesting success and other important demographic parameters prevent me from concluding that higher quality habitats are capable of sustaining the local population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 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".