Disturbance‐mediated changes to boreal mammal spatial networks in industrializing landscapes
Bibliographic record
Abstract
Compound effects of anthropogenic disturbances on wildlife emerge through a complex network of direct responses and species interactions. Land-use changes driven by energy and forestry industries are known to disrupt predator-prey dynamics in boreal ecosystems, yet how these disturbance effects propagate across mammal communities remains uncertain. Using structural equation modeling, we tested disturbance-mediated pathways governing the spatial structure of multipredator multiprey boreal mammal networks across a landscape-scale disturbance gradient within Canada's Athabasca oil sands region. Linear disturbances had pervasive direct effects, increasing site use for all focal species, except black bears and threatened caribou, in at least one landscape. Conversely, block (polygonal) disturbance effects were negative but less common. Indirect disturbance effects were widespread and mediated by caribou avoidance of wolves, tracking of primary prey by subordinate predators, and intraguild dependencies among predators and large prey. Context-dependent responses to linear disturbances were most common among prey and within the landscape with intermediate disturbance. Our research suggests that industrial disturbances directly affect a suite of boreal mammals by altering forage availability and movement, leading to indirect effects across a range of interacting predators and prey, including the keystone snowshoe hare. The complexity of network-level direct and indirect disturbance effects reinforces calls for increased investment in addressing habitat degradation as the root cause of threatened species declines and broader ecosystem 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".