Beyond habitat loss: How landscape configuration drives mammal distributions across petroleum extraction landscapes
Bibliographic record
Abstract
Abstract Anthropogenic development alters the composition and configuration of landscapes, but the effects of landscape configurations on wildlife are rarely investigated because configuration is often considered influential only beyond upper thresholds of habitat loss. In the Oil Sands Region (OSR) of Alberta, Canada—a vast area of the north‐western boreal forest—petroleum extraction impacts boreal mammals but the ecological mechanisms underlying these impacts remain largely unknown, particularly in the context of the novel landscape configurations characteristic of this region. We investigated how the configuration and composition of landscapes resulting from development in the OSR influence seven mammal species. We sampled species occurrences using camera traps deployed across a gradient of low and high disturbance landscapes; generalized linear models relate species occurrences to measures of configuration and composition. Despite only ~16% of total habitat loss in the OSR, landscape configuration was an important predictor of occurrence for multiple species. Fisher, coyote and wolf occurrences were best predicted by models representing landscape configuration, while lynx and white‐tailed deer were best predicted by landscape composition. Landscapes configured into small patches with high concentrations of anthropogenic feature edges (i.e. dense seismic lines in grid formations) were associated with greater wolf occurrences, likely due to enhanced landscape connectivity. Coyote occurred more frequently, and fishers less frequently, in small, dispersed habitat patches. Synthesis and applications. Landscape policy and management should mitigate habitat loss in anthropogenically disturbed landscapes but also consider resulting configurations from development. Complex ecological impacts are not well represented in the simple quantitative measures of disturbance currently employed: managing both composition and configuration is necessary to conserve species. In petroleum extraction regions, the arrangement of habitat patches into dense grid‐patterned linear feature networks redistributes species across landscapes, differentially impacting each species. This nuanced understanding of landscapes will inform more effective conservation strategies to support diverse wildlife populations among development.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".