Effects of oil and gas development on duck nest survival in the Western Boreal Forest
Bibliographic record
Abstract
Nest survival drives population demographics of most avian species. Researchers and managers have focused studies on investigating nest success in association with climate, land use change (e.g., agriculture), and predators in the Prairie and Arctic biomes. The Boreal Forest is also an important breeding area for ducks and it has undergone rapid land use change caused by industrial development (e.g., oil and gas; forestry). However, duck nesting ecology has received little attention in this biome. Therefore, we investigated nest survival of upland nesting ducks in the Western Boreal Forest of Alberta, Canada, from 2016–2018. We evaluated how daily nest survival rates (N = 96) were affected by a suite of natural and anthropogenic variables measured at the nest-site (microhabitat) and landscape level (macrohabitat). Nest survival was low (0.212 [85% CI: 0.152–0.282]) and comparable to nest survival estimates for ducks elsewhere in North America, including the Prairies. Nest survival increased with nest age and varied annually. At the microhabitat scale, nest survival increased with greater graminoid, forb, and shrub cover at the nest. At the macrohabitat scale, habitat influenced nest survival at coarse spatial scales with lower survival for nests with greater mineral wetland cover within 2500 m and greater survival with more forest cover within 5000 m. For anthropogenic variables, nests had greater survival with increased densities of pipelines and roads within 90 and 30 m of a nest, respectively. Contrary to our predictions, we did not find evidence that oil and gas development negatively affected duck nest survival. Comparisons with research on nest-site selection reveals both adaptive and maladaptive strategies for nest survival and suggests that some resources might be selected at an adaptive peak. Our findings highlight the importance of investigating the effects of anthropogenic disturbance at multiple scales and life history stages to gain a more nuanced understanding of species responses to land use change.
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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 | 0.001 |
| 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".