Energy infrastructure clears the way for coyotes in Alberta's oil sands
Bibliographic record
Abstract
Energy extraction and development are fragmenting the landscape in Canada’s oil sands region, creating patches of boreal forest connected by millions of kilometres of cleared linear features. The impacts of oil and gas disturbance on some wildlife species, like caribou and wolves, have been a topic of much research; yet, the influence of energy development on other species, like coyotes – which have recently expanded into the boreal forest and established strong populations – are not well understood. Here, we assessed the effects of linear features on coyote distribution and interspecific interactions, by deploying camera traps across multiple landscapes of varying energy disturbance intensities. Using an information theoretic approach, we competed hypotheses about the effects of linear feature type and density, natural feature coverage, and prey and competitor relative abundances on coyote monthly occurrence. High densities of wide linear features, and high relative abundances of small mammal prey and large competitors, best-predicted coyote occurrence, while natural features had a negative effect. Selection for higher densities of these features suggest that wide linear clearings, like roads and geo-survey seismic lines, provide movement paths for coyotes as they do for wolves, although they may also provide prey subsidies. Snowshoe hare and red squirrel prey, but not ungulates, had a strong positive effect on coyote occurrence, although coyote-prey relationships could shift with the hare cycle. Coyotes appeared to coexist with wolf and lynx competitors, perhaps through shared use of abundant resources and temporal segregation or mediated by large coyote populations – potentially indicating a departure from top-down coyote suppression by dominant heterospecifics. Energy development has fundamentally reshaped the boreal forest of Canada’s oil sands region, giving way to landscapes that support generalist, range-expanding species like coyotes, and altering community dynamics.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".